Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature

✍ Scribed by Ke-Lin Du, M. N. S. Swamy (auth.)

• Publisher: Birkhäuser Basel
• Year: 2016
• Tongue: English
• Leaves: 437
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxi
Introduction....Pages 1-28
Simulated Annealing....Pages 29-36
Genetic Algorithms....Pages 37-69
Genetic Programming....Pages 71-82
Evolutionary Strategies....Pages 83-91
Differential Evolution....Pages 93-103
Estimation of Distribution Algorithms....Pages 105-119
Topics in Evolutinary Algorithms....Pages 121-152
Particle Swarm Optimization....Pages 153-173
Artificial Immune Systems....Pages 175-189
Ant Colony Optimization....Pages 191-199
Bee Metaheuristics....Pages 201-216
Bacterial Foraging Algorithm....Pages 217-225
Harmony Search....Pages 227-235
Swarm Intelligence....Pages 237-263
Biomolecular Computing....Pages 265-281
Quantum Computing....Pages 283-293
Metaheuristics Based on Sciences....Pages 295-314
Memetic Algorithms....Pages 315-325
Tabu Search and Scatter Search....Pages 327-336
Search Based on Human Behaviors....Pages 337-346
Dynamic, Multimodal, and Constrained Optimizations....Pages 347-369
Multiobjective Optimization....Pages 371-412
Back Matter....Pages 413-434

✦ Subjects

Computational Science and Engineering;Algorithms;Optimization;Simulation and Modeling;Computational Intelligence