Multi-Objective Optimization using Artificial Intelligence Techniques
1st Edition
Published on 24. July 2019
XI, 58 pages
978-3-030-24835-2 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 58 p. 26 illus., 25 illus. in color.
File size
4,77 MB
ISBN-13
978-3-030-24835-2 (9783030248352)
DOI
10.1007/978-3-030-24835-2
Schweitzer Classification
Other editions
Additional editions
Seyedali Mirjalili | Jin Song Dong
Multi-Objective Optimization using Artificial Intelligence Techniques
Book
08/2019
Springer
€69.54
Shipment within 7-9 days
Content
- Intro
- Preface
- Contents
- Acronyms
- 1 Introduction to Multi-objective Optimization
- 1.1 Introduction
- 1.2 Uninformed and Heuristic AI Search Methods
- 1.3 Popularity of AI Heuristics and Metaheuristics
- 1.4 Exploration Versus Exploitation in Heuristics and Metaheuristics
- 1.5 Different Methods of Multi-objective Search (Optimization)
- 1.6 Scope and Structure of the Book
- References
- 2 What is Really Multi-objective Optimization?
- 2.1 Introduction
- 2.2 Essential Definitions
- 2.3 A Classification f Multi-objective Optimization Algorithms
- 2.4 A Priori Multi-objective Optimization
- 2.5 A Posteriori Multi-objective Optimization
- 2.6 Interactive Multi-objective Optimization
- 2.7 Conclusion
- References
- 3 Multi-objective Particle Swarm Optimization
- 3.1 Introduction
- 3.2 Particle Swarm Optimization
- 3.3 Multi-objective Particle Swarm Optimization
- 3.4 Results
- 3.4.1 The Impact of the Mutation Rate
- 3.4.2 The Impact of the Inertial Weight
- 3.4.3 The Impact of Personal (c1) and Social (c2) Coefficients
- 3.5 Conclusion
- References
- 4 Non-dominated Sorting Genetic Algorithm
- 4.1 Introduction
- 4.2 Multi-objective Genetic Algorithm
- 4.3 Results
- 4.3.1 The Impact of the Mutation Rate (Pm)
- 4.3.2 The Impact of the Crossover Rate (Pc)
- 4.3.3 Conclusion
- References
- 5 Multi-objective Grey Wolf Optimizer
- 5.1 Introduction
- 5.2 Grey Wolf Optimizer
- 5.3 Multi-objective Grey Wolf Optimizer
- 5.4 Literature Review of MGWO
- 5.4.1 Variants
- 5.4.2 Applications
- 5.5 Results of MOGWO
- 5.5.1 The Impact of the Parameter a
- 5.5.2 The Impact of the Parameter c
- 5.6 Conclusion
- References
