Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature
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.
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2 Simulated Annealing
3 Genetic Algorithms
4 Genetic Programming
5 Evolutionary Strategies
6 Differential Evolution
7 Estimation of Distribution Algorithms
8 Topics in Evolutinary Algorithms
14 Harmony Search
15 Swarm Intelligence
16 Biomolecular Computing
17 Quantum Computing
18 Metaheuristics Based on Sciences
19 Memetic Algorithms
20 Tabu Search and Scatter Search
21 Search Based on Human Behaviors
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adaptive antibodies Appl Soft Comput artificial bee colony bacteria behavior binary cells chromosome coding congress on evolutionary constraint convergence crossover operator differential evolution distribution diversity dynamic EDAs encoded evaluation evolutionary algorithms evolutionary computation evolutionary computation CEC evolving food source foraging Gaussian genes genetic algorithms global optimization harmony search heuristic IEEE congress IEEE Trans Evol implemented improve individuals initial inspired international conference iteration learning local search membrane memetic metaheuristic method multimodal multiobjective optimization mutation operator neighborhood neural niche nondominated solutions NSGA-II objective function offspring optimal solution optimization algorithm optimization problems optimum outperforms parallel parameters Pareto front Pareto optimal particle swarm optimization performance population population-based probabilistic Proceedings of IEEE quantum random run randomly scheme search algorithm search space selection self-adaptive simplex simulated Soft Comput solving species Springer stochastic strategy swarm intelligence tabu search tion Trans Evol Comput update variable vector