Nature-Inspired Optimization Algorithms: Challenges and Open Problems

By Xin-She Yang. Journal of Computer Science, 2020

869 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7d8a2d14f8496c0c723f7cbb2357628210a237db

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Citations in the corpus

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “• How to provide a unified framework for mathematically analyzing all MAs to determine their convergence, rate of convergence, stability, and robustness? (Yang 2020) • How to optimize an algorithm’s parameters for a certain group of problems? How to alter or adjust these parameters to optimize an al”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, methodology): “he highest value found during the process is utilised to reduce the size of both the training and testing set. 3.1.2 Bat algorithm(BA). The original bat algorithm was developed by Xin-She Yang in 2010[18, 19]. The main inspirations for these works were the echolocation behavior of microbats. As BA u”
A comprehensive survey on recent metaheuristics for feature selection (2022, background): “A black-box optimization benchmark can be provided, and the results of the new algorithms can be verified on this test-bed [274, 275].”