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.

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Includes bibliographical references.

880-01 1. Introduction to algorithms -- 2. Analysis of algorithms -- 3. Random walks and optimization -- 4. Simulated annealing -- 5. Genetic algorithms -- 6. Differential evolution -- 7. Particle swarm optimization -- 8. Firefly algorithms -- 9. Cuckoo search -- 10. Bat algorithms -- Flower pollination algorithms -- 12. A framework for self-tuning algorithms -- 13. How to deal with constraints -- 14. Multi-objective optimization -- 15. Other algorithms and hybrid algorithms -- Appendices.