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by Seyedali Mirjalili (Author), Jin Song Dong (Contributor)
About the Author
Dr. Seyedali Mirjalili is a lecturer at Griffith College, Griffith University, and internationally recognised for his advances in nature-inspired artificial intelligence (AI) techniques. He is the author of five books, 100 journal articles, 20 conference papers, and 20 book chapters. With over 10000 citations and H-index of 40, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the 3rd most cited researcher in Engineering Optimisation and Robust Optimisation using AI techniques. He has been the keynote speaker of several international conferences and is serving as an associate editor of top AI journals including Applied Soft Computing, Applied Intelligence, IEEE Access, Advances in Engineering Software, and Applied Intelligence.
About this book
This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.
Brief contents
1 Introduction to Multi-objective Optimization 1
1.1 Introduction 1
1.2 Uninformed and Heuristic AI Search Methods 1
1.3 Popularity of AI Heuristics and Metaheuristics 2
1.4 Exploration Versus Exploitation in Heuristics and Metaheuristics 4
1.5 Different Methods of Multi-objective Search (Optimization) 7
1.6 Scope and Structure of the Book 8
References 8
2 What is Really Multi-objective Optimization? 11
2.1 Introduction 11
2.2 Essential Definitions 11
2.3 A Classification f Multi-objective Optimization Algorithms 14
2.4 A Priori Multi-objective Optimization 16
2.5 A Posteriori Multi-objective Optimization 18
2.6 Interactive Multi-objective Optimization. 19
2.7 Conclusion 19
References 19
3 Multi-objective Particle Swarm Optimization 21
3.1 Introduction 21
3.2 Particle Swarm Optimization 22
3.3 Multi-objective Particle Swarm Optimization 27
3.4 Results 30
3.4.1 The Impact of the Mutation Rate 30
3.4.2 The Impact of the Inertial Weight 32
3.4.3 The Impact of Personal (c1) and Social (c2) Coefficients 32
3.5 Conclusion 35
References 35
4 Non-dominated Sorting Genetic Algorithm 37
4.1 Introduction 37
4.2 Multi-objective Genetic Algorithm 38
4.3 Results 39
4.3.1 The Impact of the Mutation Rate (Pm) 39
4.3.2 The Impact of the Crossover Rate (Pc) 42
4.3.3 Conclusion 45
References 45
5 Multi-objective Grey Wolf Optimizer 47
5.1 Introduction 47
5.2 Grey Wolf Optimizer 48
5.3 Multi-objective Grey Wolf Optimizer 50
5.4 Literature Review of MGWO 52
5.4.1 Variants 52
5.4.2 Applications 53
5.5 Results of MOGWO 54
5.5.1 The Impact of the Parameter a 54
5.5.2 The Impact of the Parameter c 54
5.6 Conclusion 56
References 57
Series: SpringerBriefs in Applied Sciences and Technology
Pages: 72 pages
Publisher: Springer; 1st ed. 2020 edition (July 24, 2019)
Language: English
ISBN-10: 3030248348
ISBN-13: 978-3030248345
Springer__Multi-Objective Optimization using Artificial Intelligence Techniques.pdf
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