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• A Brief Tutorial on Optimization Problems, Optimization Algorithms, Meta-heuristics, and Swarm Intelligence.- Introductory Review of Swarm Intelligence Techniques.- Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends.- Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems.- Performance Analysis of Hybrid Memory Based Dragonfly Algorithm in Engineering Problems.
• (source: Nielsen Book Data)

Swarm Intelligence (SI) has grown significantly, both from the perspective of algorithmic development and applications covering almost all disciplines science and technology. This book emphasizes the studies of existing SI techniques, their variants and applications. The book also contains reviews of new developments in SI techniques and hybridizations. Algorithm specific studies covering basic introduction and analysis of key components of these algorithms, such as convergence, balance of solution accuracy, computational costs, tuning and control of parameters. Application specific studies incorporating the ways of designing objective functions, solution representation and constraint handling. The book also includes studies on application domain specific adaptations in the SI techniques. The book will be beneficial for academicians and researchers from various disciplines of engineering and science working in applications of SI and other optimization problems. .
(source: Nielsen Book Data)

• Front Matter
• Introduction of Swarm Intelligence
• Foundation of Swarm Intelligence
• The Particle Swarm and Collective Intelligence
• Algorithm of Swarm Intelligence
• Novel Swarm Intelligence Optimization Algorithm (SIOA)
• Swarm Cyborg
• Immune-Inspired Swarm Cybernetic Systems
• Application of Swarm Intelligence
• References
• Index

SWARM INTELLIGENCE This important authored book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation by applying these insights to solving complex engineering problems. Motivated by the capability of the biologically inspired algorithms, "Swarm Intelligence: An Approach from Natural to Artificial" focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with swarm intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems; as well as applications and interesting experiences using particle swarm optimization, which is at the heart of computational intelligence. Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms. Audience The book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and swarm intelligence.

• Front Matter
• Introduction of Swarm Intelligence
• Foundation of Swarm Intelligence
• The Particle Swarm and Collective Intelligence
• Algorithm of Swarm Intelligence
• Novel Swarm Intelligence Optimization Algorithm (SIOA)
• Swarm Cyborg
• Immune-Inspired Swarm Cybernetic Systems
• Application of Swarm Intelligence
• References
• Index

SWARM INTELLIGENCE This important authored book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation by applying these insights to solving complex engineering problems. Motivated by the capability of the biologically inspired algorithms, "Swarm Intelligence: An Approach from Natural to Artificial" focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with swarm intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems; as well as applications and interesting experiences using particle swarm optimization, which is at the heart of computational intelligence. Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms. Audience The book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and swarm intelligence.

• Foreword.- Preface.- Introduction.- Architecture.- Model.- System Output.- Presentation Format.- Challenges and Potentials.- Conclusion.- Index.- References.
• (source: Nielsen Book Data)

This book presents the recent computational developments inspired by swarms in art known as swarm art and discusses applying swarm intelligence concepts in architecture. Non-human art is a great leap in the evolution of contemporary art, removing the requirement of an artist's production from the creative process. Furthermore, it is a critical declaration in opposition to the anthropomorphic vision which is so destructive for all other life forms and the planet's ecology. When accepted and integrated into human culture, non-human art done by artificial systems or machines boosts creativity and stimulates innovative fusions. We analyze 120 swarm systems with unique and diverse conceptual contexts, agent design, and audience engagement that can be utilized as inspiration for future projects or to design new swarm algorithms by artists, architects, or computer scientists.
(source: Nielsen Book Data)

• Firefly Algorithm and its Variants in Digital Image Processing: A Comprehensive Review
• Development of firefly algorithm interface for parameter optimization of electrochemical based machining processes
• A Firefly Algorithm-based Approach for Identifying Coalitions of Energy Providers that Best Satisfy the Energy Demand
• Structural damage identification using adaptive hybride volutionary Firefly algorithm
• An automated approach for developing a Convolutional Neural Network using a modified Firefly algorithm for Image Classification.

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc.

• Introduction Sources of Inspiration Random Variables Pseudo-Random Number Generation Random Walk Chaos Chapter Conclusion Bibliography Bat Algorithm Bat Algorithm (BA) BA Variants Bat Hybridizations BA in Real-World Applications Chapter Conclusion Bibliography Artificial Fish Swarm Algorithm Fish Swarm Optimization Artificial Fish Swarm Algorithm (AFSA) Variants AFSA Hybridizations Fish Swarm in Real-World Applications Chapter Conclusion Bibliography Cuckoo Search Algorithm Cuckoo Search (CS) CS Variants CS Hybridizations CS in Real-World Applications Chapter Conclusion Bibliography Firefly Algorithm Firefly Algorithm (FFA) FFA Variant FFA Hybridizations Firefly in Real-World Applications Chapter Conclusion Bibliography Flower Pollination Algorithm Flower Pollination Algorithm (FPA) FPA Variants FPA: Hybridizations Real-World Applications of the FPA FPA in Feature Selection Chapter Conclusion Bibliography Artificial Bee Colony Optimization Artificial Bee Colony (ABC) ABC Variants ABC Hybridizations ABC in Real-World Applications Chapter Conclusion Bibliography Wolf-Based Search Algorithms Wolf Search Algorithm (WSA) Wolf Search Optimizers in Real-World Applications Chapter Conclusion Bibliography Bird's-Eye View Criteria (1) Classification According to Swarm Guide Criteria (2) Classification According to the Probability Distribution Used Criteria (3) Classification According to the Number of Behaviors Used Criteria (4) Classification According to Exploitation of Positional Distribution of Agents Criteria (5) Number of Control Parameters Criteria (6) Classification According to Either Generation of Completely New Agents per Iteration Criteria (7) Classification Based on Exploitation of Velocity Concept in the Optimization Criteria (8) Classification According to the Type of Exploration/Exploitation Used Chapter Conclusion.
• (source: Nielsen Book Data)

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: * Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible * Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers * Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design * Details the similarities, differences, weaknesses, and strengths of each swarm optimization method * Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB(R) package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.
(source: Nielsen Book Data)

• Swarm Intelligence and Nature-Inspired Computing.- Swarm-based Computing Algorithms for Optimization.- Swarm Optimization
• Ant Colony Optimization
• Differential Evolution.- Genetic Algorithm and Evolutionary Computation
• Fireworks Algorithms.- Brain Storm Optimization Algorithm.- Bacterial Foraging Optimization Algorithm.- DNA Computing Methods.- Multi-Objective Optimization.- Swarm Robotics and Multi-Agent System.- UAV Cooperation and Control.- Machine Learning
• Data Mining.- Swarm Robotics and Multi-agent System.- Deep Neural Networks
• Machine Learning
• Data Mining.- Other Optimization Applications.- Competition on Single Objective Bounded Optimization Problems.- Swarm Intelligence and Nature-Inspired Computing.- Swarm-based Computing Algorithms for Optimization.- Particle Swarm Optimization
• Ant Colony Optimization.- Genetic Algorithm and Evolutionary Computation.- Fireworks Algorithms.- Brain Storm Optimization Algorithm
• Swarm Intelligence Approach-based Applications.- Multi-Objective Optimization.
• (source: Nielsen Book Data)

This two-volume set LNCS 13344 and 13345 constitutes the proceedings of the 13th International Conference on Advances in Swarm Intelligence, ICSI 2022, which took place in Xi'an, China, in July 2022. The theme of this year's conference was "Serving Life with Swarm Intelligence".The 85 full papers presented were carefully reviewed and selected from 171 submissions. The papers of the first part cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
(source: Nielsen Book Data)

• Swarm Intelligence and Nature-Inspired Computing.- Swarm-based Computing Algorithms for Optimization.- Swarm Optimization
• Ant Colony Optimization
• Differential Evolution.- Genetic Algorithm and Evolutionary Computation
• Fireworks Algorithms.- Brain Storm Optimization Algorithm.- Bacterial Foraging Optimization Algorithm.- DNA Computing Methods.- Multi-Objective Optimization.- Swarm Robotics and Multi-Agent System.- UAV Cooperation and Control.- Machine Learning
• Data Mining.- Swarm Robotics and Multi-agent System.- Deep Neural Networks
• Machine Learning
• Data Mining.- Other Optimization Applications.- Competition on Single Objective Bounded Optimization Problems.- Swarm Intelligence and Nature-Inspired Computing.- Swarm-based Computing Algorithms for Optimization.- Particle Swarm Optimization
• Ant Colony Optimization.- Genetic Algorithm and Evolutionary Computation.- Fireworks Algorithms.- Brain Storm Optimization Algorithm
• Swarm Intelligence Approach-based Applications.- Multi-Objective Optimization.
• (source: Nielsen Book Data)

This two-volume set LNCS 13344 and 13345 constitutes the proceedings of the 13th International Conference on Advances in Swarm Intelligence, ICSI 2022, which took place in Xi'an, China, in July 2022. The theme of this year's conference was "Serving Life with Swarm Intelligence". The 85 full papers presented were carefully reviewed and selected from 171 submissions. The papers of the second part cover topics such as: Swarm Robotics and Multi-agent System; Deep Neural Networks; Machine Learning; Data Mining; Other Optimization Applications; ICSI-OC'2022: Competition on Single Objective Bounded Optimization Problems; Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Swarm Intelligence Approach-based Applications; Multi-Objective Optimization.
(source: Nielsen Book Data)

• A Geometry-Sensitive Quorum Sensing Algorithm for the Best-of-N Site Selection Problem.- An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots.- Automatic Design of Multi-Objective Particle Swarm Optimizers.- Automatic Extraction of Understandable Controllers from Video Observations of Swarm Behaviors.- Benchmarking Performances of Collective Decision-making Strategies with Respect to Communication Bandwidths in Discrete Collective Estimation.- Best-of-N Collective Decisions on a Hierarchy.- Collective Decision-making for Conflict Resolution in Multi-Agent Pathfinding.- Controlling Robot Swarm Aggregation through a Minority of Informed Robots.- Decentralized Multi-Agent Path Finding in Warehouse Environments for Fleets of Mobile Robots with Limited Communication Range.- Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping.- Dynamic Spatial Guided Multi-Guide Particle Swarm Optimization Algorithm for Many-Objective Optimization.- Extracting Symbolic Models of Collective Behaviors with Graph Neural Networks and Macro-Micro Evolution.- Learning Resilient Swarm Behaviors via Ongoing Evolution.- Mind the Gap! Predictive Flocking of Aerial Robot Swarm in Cluttered Environments.- Moving Mixtures of Active and Passive Elements with Robots That Do Not Compute.- Real-time Coordination of a Foraging Robot Swarm Using Blockchain Smart Contracts.- Robot Swarms Break Decision Deadlocks in Collective Perception through Cross-inhibition.- Self-Organized Chain Formation of Nano-Drones in an Open Space.- The Hidden Benefits of Limited Communication and Slow Sensing in Collective Monitoring of Dynamic Environments.- A Novel Time-of-Flight Range and Bearing Sensor System for Micro Air Vehicle Swarms.- An Adaptive Metric Model for Collective Motion Structures in Dynamic Environments.- An Extension of the iMOACOR Algorithm Based on Layer-Set Selection.- Binary Particle Swarm Optimization for Selective Cell Switch-Off in Ultra-Dense 5G Networks.- Choeur Synthetique: an Art Installation Based on Swarm Robotics.- Component Swarm Optimization using Virtual Forces for Solving Layout Problems.- Constant Bearing Flocking.- Distributed Sorting in Complex Environments.- Effect of Different Communication Affordances on the Emergence of Collaboration Strategies in an Online Multiplayer Game.- Generating and Analyzing Collective Step-climbing Behavior in a Multi-legged Robotic Swarm.- Modeling Immune Search through the Lymphatic Network.- Optimization of a Self-organized Collective Motion in a Robotic Swarm.- Response Threshold Distributions to Improve Best-of-N Decisions in Minimalistic Robot Swarms.- Stability-Guided Particle Swarm Optimization.- Animals Are Not Particles: Towards a Second Generation of 'Hetero-Swarm' Robotics.- Applying PSO to Find Optimal Strategy for 3D Chip Layout Design.- Particle Swarm Optimization Applied to the Direct Aperture Optimization Problem on Radiotherapy.- Search Space Illumination of Robot Swarm Parameters for Trustworthiness.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 13th International Conference on Swarm Intelligence, ANTS 2022, held in Malaga, Spain, in November 2022. The 19 full papers presented, together with 14 short papers and 4 extended abstracts were carefully reviewed and selected from 45 submissions. ANTS 2022 contributions are dealing with any aspect of swarm intelligence such as behavioral models of social insects, empirical and theoretical research in swarm intelligence, application of swarm intelligence methods, and much more.
(source: Nielsen Book Data)

• Swarm Intelligence and Nature-Inspired Computing.- Swarm-based Computing Algorithms for Optimization.- Particle Swarm Optimization.- Ant Colony Optimization.- Differential Evolution.- Genetic Algorithm and Evolutionary Computation.- Fireworks Algorithms.- Brain Storm Optimization Algorithm.- Bacterial Foraging Optimization Algorithm.- DNA Computing Methods.
• (source: Nielsen Book Data)

his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
(source: Nielsen Book Data)

• Multi-Objective Optimization.- Swarm Robotics and Multi-Agent System.- UAV Cooperation and Control.- Machine Learning.- Data Mining.- Other Applications.
• (source: Nielsen Book Data)

his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Cooperative Model of Evolutionary Algorithms and Real-World Problems
• 1 Introduction
• 2 A Cooperative Model of Evolutionary Algorithms
• 2.1 CoBiDE
• 2.2 Individual-Dependent Approach
• 2.3 CMA-ES
• 2.4 jSO
• 2.5 Competitive Mechanism and Population-Size Reduction
• 3 Experimental Settings
• 4 Results and Discussion
• 5 Conclusion
• References
• Pareto-Based Self-organizing Migrating Algorithm Solving 100-Digit Challenge
• 1 Introduction
• 2 The Self-organizing Migrating Algorithm
• 3 SOMA Pareto

• 3.1 The Organization Operation
• 3.2 The Migration Operation
• 3.3 The Update Operation
• 4 Experiment Setup
• 4.1 Hardware and Software Environment
• 4.2 The Parameters
• 4.3 Stopping Criterion
• 4.4 How to Score Points
• 5 Simulation Results
• 6 Conclusion
• References
• Population Size in Differential Evolution
• 1 Introduction
• 2 Related Work
• 3 Background
• 3.1 Differential Evolution (DE)
• 3.2 Self-adaptive Differential Evolution (jDE)
• 4 Self-adaptive Differential Evolution with Restarts (rjDE)
• 5 Experiments and Results
• 5.1 Results for NP = 100

• 5.2 Results for Different Population Sizes
• 6 Conclusion
• References
• Channel Assignment with Ant Colony Optimization
• 1 Introduction
• 2 Channel Assignment Problem
• 3 Proposed Algorithm
• 4 Experimental Research
• 4.1 Experimental Settings
• 4.2 Results
• 5 Conclusion
• References
• Self-organizing Migrating Algorithm with Non-binary Perturbation
• Abstract
• 1 Introduction
• 2 Self-organizing Migrating Algorithm (SOMA)
• 3 Experiment Setup
• 4 Results
• 4.1 Performance Visualization
• 4.2 Numerical Results
• 4.3 Parameter Pool Visualization
• 5 Conclusion

• Acknowledgements
• References
• Boundary Strategies for Self-organizing Migrating Algorithm Analyzed Using CEC'17 Benchmark
• Abstract
• 1 Introduction
• 2 Self-organizing Migrating Algorithm
• 3 Boundary Methods
• 3.1 Clipping Method
• 3.2 Random Method
• 3.3 Reflection Method
• 3.4 Periodic Method
• 4 Experiment Setting
• 5 Results
• 6 Conclusion
• Acknowledgments
• References
• MOEA with Approximate Nondominated Sorting Based on Sum of Normalized Objectives
• Abstract
• 1 Introduction
• 2 Proposed Method
• 2.1 Proposed Approximate NDS Based on Sum of Objectives

• 3 Experimental Results and Simulation Results
• 4 Conclusion
• Acknowledgement
• References
• Evolutionary Bi-objective Optimization and Knowledge Extraction for Electronic and Automotive Cooling
• 1 Introduction
• 2 Plate-Fin Heat Sink (PFHS)
• 2.1 Multi-objective Optimization Problem Formulation
• 3 Optimization Results
• 4 Knowledge Extraction
• 4.1 PFHS with Flow Through Air Cooling System
• 4.2 PFHS with Impingement Flow Air Cooling System
• 5 Conclusion
• References
• Classification of Stock Market Trends with Confidence-Based Selective Predictions
• 1 Introduction

This volume constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019, held in Maribor, Slovenia, in July 2019. The 18 full papers presented in this volume were carefully reviewed and selected from a total of 31 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering. -- Provided by publisher.

• Novel Models and Algorithms for Optimization
• Generative Adversarial Optimization
• Digital Model of Swarm Unit System with Interruptions
• Algorithm Integration Behavior for Discovering Group Membership Rules
• Success History Based Position Adaptation in Co-Operation of Biology Related Algorithms
• An Inter-Peer Communication Mechanism Based Water Cycle Algorithm
• Cooperation-based Gene Regulatory Network for Target Entrapment
• Population-based Metaheuristics for Planning Interval Training Sessions in Mountain Biking
• Comparison of Infrastructure and Adhoc Modes in Survivable Networks Enabled by Evolutionary Swarms
• Particle Swarm Optimization
• An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm
• Parameters Optimization Of Relay Self-Oscillations Sampled Data Controller Based On Particle Swarm Optimization
• Niching Particle Swarm Optimizer with Entropy-based Exploration Strategy for Global Optimization
• A Study on Designing an Aperiodic Antenna Array using Boolean PSO
• Building Energy Performance Optimization: A New Multi-Objective Particle Swarm Method
• A Novel PSOEDE Algorithm for Vehicle Scheduling Problem in Public Transportation
• Hierarchical Competition Framework for Particle Swarm Optimization
• Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader
• Variants and Parameter Investigations of Particle Swarm Optimisation for Solving Course Timetabling Problems
• Ant Colony Optimization
• Multiple Start Modifications Of Ant Colony Algorithm For Multiversion Software Design
• Ant Colony Algorithm for Cell Tracking Based on Gaussian Cloud Model
• Physarum-based Ant Colony Optimization for Graph Coloring Problem
• Ant Colony Algorithm Based Scheduling with Lot-sizing for Printed Circuit Board Assembly Shop
• Variable Speed Robot Navigation by an ACO Approach
• Solving Scheduling Problem in PCB Assembly and Its Optimization Using ACO
• Fi reworks Algorithms and Brain Storm Optimization
• Accelerating Fireworks Algorithm with Weight-based Guiding Sparks
• Last-position Elimination-based Fireworks Algorithm for Function Optimization
• Planar Thinned Antenna Array Synthesis Using Modified Brain Storm Optimization
• Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Fast Brain Storm Optimization
• Swarm Intelligence Algorithms and Improvements
• Automatic Diet Generation by Artificial Bee Colony Algorithm
• A Multi-strategy Artificial Bee Colony Algorithm with Neighborhood Search
• Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves
• Quantum Behaved Fruit Fly Optimization Algorithm for Continuous Function Optimization Problems
• Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with The Bat Algorithm
• Bacterial Foraging Optimization with Memory and Clone Schemes for Dynamic Environments
• Genetic Algorithm and Differential Evolution
• Evaluation of Genetic Algorithm and Hybrid Genetic Algorithm-Hill Climbing with Elitist for Lecturer University Timetabling Problem
• Federated Learning Assisted Interactive EDA with Dual Probabilistic Models for Personalized Search
• Second Order Differential Evolution for Constrained Optimization
• Computability and Stability for Hybrid Algorithms
• Swarm Robotics
• Stochastic Self-Organizing Control for Swarm Robot Systems
• Framework for Evaluation of Swarm-based Chemical Reaction Optimization Algorithm
• Mixed Game Pigeon-inspired Optimization for Unmanned Aircraft System Swarm Formation
• Research on UAV Task Assignment Method Based on Parental Genetic Algorithm
• A Comparison among the Denavit
• Hartenberg, the Screw Theory, and the Iterative Methods to Solve Inverse Kinematics for Assistant Robot Arm.

The two-volume set of LNCS 11655 and 11656 constitutes the proceedings of the 10th International Conference on Advances in Swarm Intelligence, ICSI 2019, held in Chiang Mai, Thailand, in June 2019. The total of 82 papers presented in these volumes was carefully reviewed and selected from 179 submissions. The papers were organized in topical sections as follows: Part I: Novel methods and algorithms for optimization; particle swarm optimization; ant colony optimization; fireworks algorithms and brain storm optimization; swarm intelligence algorithms and improvements; genetic algorithm and differential evolution; swarm robotics. Part II: Multi-agent system; multi-objective optimization; neural networks; machine learning; identification and recognition; social computing and knowledge graph; service quality and energy management.

• Multi-agent System.- Multi-robot Cooperation Strategy in a Partially Observable Markov Game Using Enhanced Deep Deterministic Policy Gradient.- Research on the Construction of Underwater Platform Combat Deduction System Based on Service-Oriented and Multi-Agent Technology.- Context-aware Layered Learning for Argumentation Based Multiagent Collaborative Recognition.- TH-GRN Model Based Collective Tracking in Confined Environment.- Multi-Objective Optimization.- Multi-objective Optimization of a Steering Linkage Using Alternative Objective Functions.- Using two reproduction operators for Balancing Convergence and Diversity in MOEA/D.- A Surrogate-Assisted Improved Many-Objective Evolutionary Algorithm.- Research of Multi-objective Personalized Recommendation Algorithm Based on Multi-thread Concurrency.- Multi-criteria Recommender Systems Based on Multi-objective Hydrologic Cycle Optimization.- Neural Networks.- Convolutional Neural Network Inception-
• v3: A Machine Learning Approach for Leveling Short-Range Rainfall Forecast Model from Satellite Image.- Application of Convolutional Neural Network in Object Recognition of Remote Sensing Image.- Paragraph Coherence Detection Model Based on Recurrent Neural Networks.- Use of Artificial Neural Networks in Determining Domestic Violence Predictors.- Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features.- Novel Algorithm for Blind Classification of Space-Time Block Codes in cognitive radio.- Spiking Neural Models and Their Application in DNA Microarrays Classification.- An Unified View on the Feedforward Neural Network Architecture.- Machine Learning.- Efficient Android Phishing Detection Based on Improved Naive Bayes Algorithm.- Parkinson Disease Analysis Using Supervised and Unsupervised Techniques.- Implementation of the Eclipse Process Framework Composer Tool for the Documentation of Quality Management Systems: A Case Applied in Healthcare Services.- Enkephalon - Technological Platform to Support the Diagnosis of Alzheimer's Disease through the Analysis of Resonance Images Using Data Mining Techniques.- Experience on Learning through an AR-based Course.- Identification and Recognition.- Seismograph Design for Landslide Monitoring in The Andean Region Using Automatic Control Techniques and Mathematical Modeling.- Improving Chinese Named Entity Recognition with Semantic Information of Character Multi-position Representation.- The Discourse Structure Recognition Model Based on Text Classification.- Ballistic Wind Speed Identification Method Based on Hierarchical Optimization Algorithm.- BackgroundNet: Small Dataset-based Object Detection in Stationary Scenes.- A New Method for Identification of Essential Proteins by Information Entropy of Protein Complex and Subcellular Localization.- Research on Fault Diagnosis Method Based on RSAPSO-DBN.- Standard Modeling Practice Research for A Safety Technical Disclosure of Wind Turbine Maintenance Systems.- Social Computing and Knowledge Graph.- The Critical Factor Prompting the Usage of a Social Computing.- Social Coalition-based V2V Broadcasting Optimization Algorithm in VANETs.- An Interpretable Recommendations Approach Based On User Preferences And Knowledge Graph.- WSIA: Web Ontological Search Engine based on Smart Agents Applied to Scientific Articles.- Service Quality and Energy Management.- Record Management in the Cloud: Service Quality and Service Level Agreement.- Recovering Scale in Monocular DSO Using Multi-sensor Data.- Energy Management Strategy (EMS) for Hybrid Electric Vehicles Based on Safe Experimentation Dynamics (SED).- Serial Interface Converter of Micromechanical Sensors to a Parallel Interface.- The Location Privacy Preserving Scheme Based on Hilbert Curve for Indoor LBS.- SSwWS: Structural Model of Information Architecture.
• (source: Nielsen Book Data)

• Novel Models and Algorithms for Optimization.- Generative Adversarial Optimization.- Digital Model of Swarm Unit System with Interruptions.- Algorithm Integration Behavior for Discovering Group Membership Rules.- Success History Based Position Adaptation in Co-Operation of Biology Related Algorithms.- An Inter-Peer Communication Mechanism Based Water Cycle Algorithm.- Cooperation-based Gene Regulatory Network for Target Entrapment.- Population-based Metaheuristics for Planning Interval Training Sessions in Mountain Biking.- Comparison of Infrastructure and Adhoc Modes in Survivable Networks Enabled by Evolutionary Swarms.- Particle Swarm Optimization.- An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm.- Parameters Optimization Of Relay Self-Oscillations Sampled Data Controller Based On Particle Swarm Optimization.- Niching Particle Swarm Optimizer with Entropy-based Exploration Strategy for Global Optimization.- A Study on Designing an Aperiodic Antenna Array using Boolean PSO.- Building Energy Performance Optimization: A New Multi-Objective Particle Swarm Method.- A Novel PSOEDE Algorithm for Vehicle Scheduling Problem in Public Transportation.- Hierarchical Competition Framework for Particle Swarm Optimization.- Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader.- Variants and Parameter Investigations of Particle Swarm Optimisation for Solving Course Timetabling Problems.- Ant Colony Optimization.- Multiple Start Modifications Of Ant Colony Algorithm For Multiversion Software Design.- Ant Colony Algorithm for Cell Tracking Based on Gaussian Cloud Model.- Physarum-based Ant Colony Optimization for Graph Coloring Problem.- Ant Colony Algorithm Based Scheduling with Lot-sizing for Printed Circuit Board Assembly Shop.- Variable Speed Robot Navigation by an ACO Approach.- Solving Scheduling Problem in PCB Assembly and Its Optimization Using ACO.- Fireworks Algorithms and Brain Storm Optimization.- Accelerating Fireworks Algorithm with Weight-based Guiding Sparks.- Last-position Elimination-based Fireworks Algorithm for Function Optimization.- Planar Thinned Antenna Array Synthesis Using Modified Brain Storm Optimization.- Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Fast Brain Storm Optimization.- Swarm Intelligence Algorithms and Improvements.- Automatic Diet Generation by Artificial Bee Colony Algorithm.- A Multi-strategy Artificial Bee Colony Algorithm with Neighborhood Search.- Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves.- Quantum Behaved Fruit Fly Optimization Algorithm for Continuous Function Optimization Problems.- Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with The Bat Algorithm.- Bacterial Foraging Optimization with Memory and Clone Schemes for Dynamic Environments.- Genetic Algorithm and Differential Evolution.- Evaluation of Genetic Algorithm and Hybrid Genetic Algorithm-Hill Climbing with Elitist for Lecturer University Timetabling Problem.- Federated Learning Assisted Interactive EDA with Dual Probabilistic Models for Personalized Search.- Second Order Differential Evolution for Constrained Optimization.- Computability and Stability for Hybrid Algorithms.- Swarm Robotics.- Stochastic Self-Organizing Control for Swarm Robot Systems.- Framework for Evaluation of Swarm-based Chemical Reaction Optimization Algorithm.- Mixed Game Pigeon-inspired Optimization for Unmanned Aircraft System Swarm Formation.- Research on UAV Task Assignment Method Based on Parental Genetic Algorithm.- A Comparison among the Denavit - Hartenberg, the Screw Theory, and the Iterative Methods to Solve Inverse Kinematics for Assistant Robot Arm.
• (source: Nielsen Book Data)

The two-volume set of LNCS 11655 and 11656 constitutes the proceedings of the 10th International Conference on Advances in Swarm Intelligence, ICSI 2019, held in Chiang Mai, Thailand, in June 2019. The total of 82 papers presented in these volumes was carefully reviewed and selected from 179 submissions. The papers were organized in topical sections as follows: Part I: Novel methods and algorithms for optimization; particle swarm optimization; ant colony optimization; fireworks algorithms and brain storm optimization; swarm intelligence algorithms and improvements; genetic algorithm and differential evolution; swarm robotics. Part II: Multi-agent system; multi-objective optimization; neural networks; machine learning; identification and recognition; social computing and knowledge graph; service quality and energy management.
(source: Nielsen Book Data)

• Multi-objective Optimization
• A Parametric Study of Crossover Operators in Pareto-based Multiobjective Evolutionary Algorithm
• Non-dominated Sorting and Crowding Distance Based Multi-objective Chaotic Evolution
• On Performance Improvement based on Restart Meta-Heuristic Implementation for Solving Multi-Objective Optimization Problems
• Using multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVs
• Improved Interval Multi-objective Evolutionary Optimization Algorithm based on Directed Graph
• A Novel Linear Time Invariant Systems Order Reduction Approach based on Cooperative Multi-Objective Genetic Algorithm
• Solving Constrained Multi-objective Optimization Problems with Evolutionary Algorithms
• Portfolio Optimization
• Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Optimization
• Metaheuristics for Portfolio Optimization
• Community Detection
• Community Detection under Exponential Random Graph Model: A Metaheuristic Approach
• An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection
• The Design and Development of the Virtual Learning Community for Teaching Resources Personalized Recommendation
• The Effect of Event Sentiment on Product Recommendation in a Microblog Platform
• Multi-Agent Systems and Swarm Robotics
• Solar Irradiance Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet
• Passive Field Dynamics Method: an Advanced Physics-based Approach for Formation Control of Robot Swarm
• Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics
• Swarm-based Spreading Points
• A Survivability Enhanced Swarm Robotic Searching System Using Multi-Objective Particle Swarm Optimization
• Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm
• Building Fractals with a Robot Swarm
• A Stigmergy Based Search Method for Swarm Robots
• Cooperative Control of Multi-Robot System Using Mobile Agent for Multi Source Localization
• Hybrid Optimization Algorithms and Applications
• Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following through the Fusion of Continuous ACO and PSO
• Optimal Operational Planning of Energy Plants by Multi-population Differential Evolutionary Particle Swarm Optimization
• A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony
• Study on Greedy Search to Improve Simulated Annealing Algorithm for Large-scale Traveling Salesman Problem
• A Hybrid Swarm based Composition for Chinese Music
• Fuzzy and Swarm Approach
• Fuzzy Logic Controller Design for Tuning the Cooperation of Bio-Inspired Algorithms
• Making Capital Budgeting Decisions for Project Abandonment by Fuzzy Approach
• An Imputation for Missing Data Features based on Fuzzy Swarm Approach in Heart Disease Classification
• Clustering and Forecast
• Total Optimization of Smart City Using Initial Searching Points Generation Based on k-means Algorithm
• Clustering Analysis of ECG Data Streams
• A Novel Multi-cell Multi-Bernoulli Tracking Method Using Local Fractal Feature Estimation
• An Improved Locality Preserving Projection Method for Dimensionality Reduction with Hyperspectral Image
• Applying a Classification Model for Selecting Postgraduate Programs
• University Restaurant Sales Forecast Based on BP neural Network
• In Shanghai Jiao Tong University Case
• Classification and Detection
• Swarm ANN/SVR-based modeling method for warfarin dose prediction in Chinese
• A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting
• An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning
• Fast Pseudo Random Forest using Discrimination Hyper-space
• A Fast Video Vehicle Detection Approach based on Improved Adaboost Classifier
• Detection of Repetitive Forex Chart Patterns
• Damage Estimation from Cues of Image Change
• Identifying Deceptive Review Comments with Rumor and Lie Theories
• Identifying Fake Review Comments for Hostel Industry
• Planning and Routing Problems
• Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy
• Optimal Micro-Siting Planning Considering Long-Term Electricity Demand
• A Hyper-Heuristic Method for UAV Search Planning
• An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems
• A Method of New Workers' Accurate Acceptance of Tasks Using Capable Sensing
• A Genetic Mission Planner for Solving Temporal Multi-agent Problems with Concurrent Tasks
• Reformulation and Metaheuristic for the Team Orienteering Arc Routing Problem
• Application of Smell Detection Agent Based Algorithm for Optimal Path Identification by SDN Controllers
• A Comparison of Heuristic Algorithms for Bus Dispatch
• Simulation and Application of Algorithms CVRP to Optimize the Transport of Minerals Metallic and Nonmetallic by Rail for Export
• Dialog System Applications
• User Intention Classification in An Entities Missed In-vehicle Dialog System
• An Exploratory Study of Factors Affecting Number of Fans on Facebook Based on Dialogic Theory
• Assembling Chinese-Mongolian Speech Corpus via Crowdsourcing
• Robotic Control
• Developing Robot Drumming Skill with Listening-Playing Loop
• Evaluation of Parameters of Transactions When Remote Robot Control
• Desktop Gestures Recognition for Human Computer Interaction
• Approach to the Diagnosis and Configuration of Servo Drives in Heterogeneous Machine Control Systems
• Other Applications
• Gravitational Search Algorithm in Recommendation Systems
• A Driver Model Based on Emotions
• A Binaural Signal Synthesis Approach for Fast Rendering of Moving Sound
• Semantic Evolutionary Visualization. .

This book and its companion volumes, LNCS volumes 10385 and 10386, constitute the proceedings of the 8th International Conference on Swarm Intelligence, ICSI 2017 held from July 27 to August 1, 2017, in Fukuoka, Japan. ICSI 2017 was held in conjunctionwith the Second International Conference on Data Mining and Big Data (DMBD 2017)at Fukuoka, Japan, to share common mutual ideas, promote transverse fusion, and stimulate innovation. ICSI 2017 received 267 submissions from about 512 authors in 35 countries and regions across six continents. Each submission was reviewed by at least two, and on average 2.9 reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 133 high-quality papers were selected for publication in this proceedings volume, with an acceptance rate of 49.81%. The papers are organized in 24 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development.

• Novel swarm-based optimization algorithms and applications
• Particle swarm optimization
• Ant colony optimization
• Artificial bee colony algorithms
• Evolutionary and genetic algorithms
• Differential evolution
• Brain storm optimization algorithm
• Biogeography based optimization
• Cuckoo search
• Hybrid methods
• Multi-objective optimization
• Multi-agent systems and swarm robotics
• Neural networks and fuzzy methods
• Data mining approaches
• Information security
• Automation control
• Combinatorial optimization algorithms
• Scheduling and path planning
• Machine learning
• Blind sources separation
• Swarm interaction behavior
• Parameters and system optimization
• Neural networks
• Evolutionary and genetic algorithms
• Fuzzy systems
• Forecasting algorithms
• Classification
• Tracking analysis
• Simulation
• Image and texture analysis
• Dimension reduction
• System optimization
• Segmentation and detection system
• Machine translation
• Virtual management and disaster analysis.

This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis.

• Novel swarm-based optimization algorithms and applications
• Particle swarm optimization
• Ant colony optimization
• Artificial bee colony algorithms
• Evolutionary and genetic algorithms
• Differential evolution
• Brain storm optimization algorithm
• Biogeography based optimization
• Cuckoo search
• Hybrid methods
• Multi-objective optimization
• Multi-agent systems and swarm robotics
• Neural networks and fuzzy methods
• Data mining approaches
• Information security
• Automation control
• Combinatorial optimization algorithms
• Scheduling and path planning
• Machine learning
• Blind sources separation
• Swarm interaction behavior
• Parameters and system optimization
• Neural networks
• Evolutionary and genetic algorithms
• Fuzzy systems
• Forecasting algorithms
• Classification
• Tracking analysis
• Simulation
• Image and texture analysis
• Dimension reduction
• System optimization
• Segmentation and detection system
• Machine translation
• Virtual management and disaster analysis.

This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis.

• Novel swarm-based optimization algorithms and applications
• Particle swarm optimization
• Ant colony optimization
• Artificial bee colony algorithms
• Evolutionary and genetic algorithms
• Differential evolution
• Brain storm optimization algorithm
• Biogeography based optimization
• Cuckoo search
• Hybrid methods
• Multi-objective optimization
• Multi-agent systems and swarm robotics
• Neural networks and fuzzy methods
• Data mining approaches
• Information security
• Automation control
• Combinatorial optimization algorithms
• Scheduling and path planning
• Machine learning
• Blind sources separation
• Swarm interaction behavior
• Parameters and system optimization
• Neural networks
• Evolutionary and genetic algorithms
• Fuzzy systems
• Forecasting algorithms
• Classification
• Tracking analysis
• Simulation
• Image and texture analysis
• Dimension reduction
• System optimization
• Segmentation and detection system
• Machine translation
• Virtual management and disaster analysis.

This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis.

• Novel swarm-based search methods.- Novel optimization algorithm.- Particle swarm optimization.- Ant colony optimization for travelling salesman problem.- Artificial bee colony algorithms.- Artificial immune system.- Evolutionary algorithms.- Neural networks and fuzzy methods.- Hybrid methods.- Multi-objective optimization.- Multi-agent systems.- Evolutionary clustering algorithms.- Classification methods.- GPU-based methods.- Scheduling and path planning.- Wireless sensor networks.- Power system optimization.- Swarm intelligence in image and video processing.- Applications of swarm intelligence to management problems.- Swarm intelligence for real-world application.
• (source: Nielsen Book Data)

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.
(source: Nielsen Book Data)

• Novel swarm-based search methods.- Novel optimization algorithm.- Particle swarm optimization.- Ant colony optimization for travelling salesman problem.- Artificial bee colony algorithms.- Artificial immune system.- Evolutionary algorithms.- Neural networks and fuzzy methods.- Hybrid methods.- Multi-objective optimization.- Multi-agent systems.- Evolutionary clustering algorithms.- Classification methods.- GPU-based methods.- Scheduling and path planning.- Wireless sensor networks.- Power system optimization.- Swarm intelligence in image and video processing.- Applications of swarm intelligence to management problems.- Swarm intelligence for real-world application.
• (source: Nielsen Book Data)

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.
(source: Nielsen Book Data)