Nature-Inspired Optimization AlgorithmsElsevier, 17 févr. 2014 - 300 pages Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. - Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature - Provides a theoretical understanding as well as practical implementation hints - Provides a step-by-step introduction to each algorithm |
Table des matières
| 23 | |
Random Walks and Optimization | 45 |
Simulated Annealing | 67 |
Genetic Algorithms | 77 |
Differential Evolution | 89 |
Particle Swarm Optimization | 99 |
Firefly Algorithms | 111 |
Cuckoo Search | 129 |
Flower Pollination Algorithms | 155 |
A Framework for SelfTuning Algorithms | 175 |
How to Deal with Constraints | 183 |
MultiObjective Optimization | 197 |
Other Algorithms and Hybrid Algorithms | 213 |
APPENDIX A Test Function Benchmarks | 227 |
APPENDIX B Matlab Programs | 247 |
Bat Algorithms | 141 |
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Expressions et termes fréquents
analysis applications bat algorithm bees behavior best solution constraints crossover cuckoo search current best current global best developed differential evolution discrete eagle strategy efficient Evol Comput evolutionary algorithms example exploitation exploration firefly algorithm Fister flower pollination formulated Function f(x Gaussian genetic algorithms global convergence global minimum global optimality harmony search hybrid IEEE implementation initial Lévy distribution Lévy flights Markov chain mathematical metaheuristic algorithms method Minimize minimum is located multimodal mutation nature-inspired algorithms Nature-Inspired Optimization Algorithms nonlinear number of iterations objective function optimal solution parameter tuning Pareto front particle swarm optimization performance pheromone pollination population probability random number random variable random walk randomly rithms search algorithm search space selection self-organization simulated annealing solve step stochastic studies subspace swarm intelligence test functions updating values variants vector worth pointing x₁ Xin-She xt+1