Search and Optimization by Metaheuristics

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This textbook offers a thorough introduction to nature-inspired metaheuristic methods for search and optimization, highlighting the latest trends in evolutionary algorithms and natural computing. It discusses over 100 different methods in detail, focusing on non-standard optimization problems and progressing from basic to more complex concepts. An introductory chapter provides essential biological and mathematical backgrounds for understanding the material. Subsequent chapters examine major metaheuristics derived from natural phenomena, including simulated annealing, genetic algorithms, differential evolution, particle swarm optimization, and more. General topics such as dynamic, multimodal, constrained, and multiobjective optimizations are also covered. Each chapter features detailed flowcharts illustrating specific algorithms and exercises to reinforce key concepts. The appendix includes benchmarks for evaluating metaheuristics. This work is designed primarily as a textbook for graduate and advanced undergraduate students in engineering and computer science but will also be a valuable resource for scientists and researchers in these fields, as well as anyone interested in search and optimization methods.