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.
ISBN: | 9783030248345 |
Publication date: | 7th August 2019 |
Author: | Seyedali Mirjalili, Jin Song Dong |
Publisher: | Springer an imprint of Springer International Publishing |
Format: | Paperback |
Pagination: | 58 pages |
Series: | SpringerBriefs in Applied Sciences and Technology |
Genres: |
Artificial intelligence Machine learning Management decision making Operational research |