Multi-Objective Optimization using Artificial Intelligence Techniques /

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Bibliographic Details
Author / Creator:	Mirjalili, Seyedali, author.
Imprint:	Cham : Springer, [2020]
Description:	1 online resource (XI, 58 pages) : illustrations (some color)
Language:	English
Series:	SpringerBriefs in applied sciences and technology. Computational intelligence, 2625-3704 SpringerBriefs in applied sciences and technology. Computational intelligence.
Subject:	Mathematical optimization. Artificial intelligence. Artificial intelligence. Mathematical optimization.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12601677

Hidden Bibliographic Details
Other authors / contributors:	Dong, Jin Song, 1967- author.
ISBN:	9783030248352 3030248356
Notes:	Includes bibliographical references. Online resource; title from digital title page (viewed on April 30, 2020).
Summary:	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.
Standard no.:	10.1007/978-3-030-24

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