Search and Optimization by Metaheuristics

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.
An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.
Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

Offers a comprehensive and state-of-the-art introduction to nature-inspired metaheuristics Includes detailed, implementable algorithmic flowcharts for the most popular algorithms Discusses over 100 different types of nature-inspired search and optimization methods Will allow students to discover the newest trends in metaheuristics and optimization Includes supplementary material: sn.pub/extras

Autorentext
Ke-Lin Du, PhD, is Affiliate Associate Professor at Concordia University, Montreal, Quebec, Canada, and Founder and CEO of Xonlink Inc, Ningbo, China.
M.N.S. Swamy, PhD, is Research Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Quebec, Canada.

Inhalt
Preface.- Introduction.- Simulated Annealing.- Optimization by Recurrent Neural Networks.- Genetic Algorithms and Genetic Programming.- Evolutionary Strategies.- Differential Evolution.- Estimation of Distribution Algorithms.- Mimetic Algorithms.- Topics in EAs.- Particle Swarm Optimization.- Artificial Immune Systems.- Ant Colony Optimization.- Tabu Search and Scatter Search.- Bee Metaheuristics.- Harmony Search.- Biomolecular Computing.- Quantum Computing.- Other Heuristics-Inspired Optimization Methods.- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations.- Multiobjective Optimization.- Appendix 1: Discrete Benchmark Functions.- Appendix 2: Test Functions.- Index.