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 literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm
Explore Nature-Inspired Optimization Algorithms by Xin-She Yang on eBooksStore by Arnlweb. Discover book details, reader ratings, reviews, release information, genres, and related digital books available through the iTunes Store.
This book is part of our growing collection of bestselling eBooks, popular digital reading materials, and trending author releases. Readers can explore similar books, discover new authors, and browse related genres including fiction, romance, mystery, fantasy, business, self-help, educational books, and more.
Our platform helps readers discover highly rated digital books optimized for smartphones, tablets, laptops, and desktop devices. Browse fast-loading book pages, reader reviews, and popular recommendations from bestselling authors worldwide.