Overview
- Offers a concise guide to the most important multi-objective optimization techniques
- Discusses in detail several experimental results
- The source codes for all the proposed algorithms are provided on a dedicated webpage
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (5 chapters)
Keywords
- MOGWO Algorithm
- NSGA-II
- MOPSO
- Using Multiobjective Algorithms
- Multi-Objective Optimization Algorithms
- Interactive Multi-Objective Optimization
- Techniques for Decision Making
- Pareto Optimality Dominance
- Posteriori Multi-Objective Optimization
- Impact of Mutation Rate
- Performance of Genetic Algorithms
- PSO Algorithm
- Evolutionary Optimization Algorithms
About this book
Authors and Affiliations
Bibliographic Information
Book Title: Multi-Objective Optimization using Artificial Intelligence Techniques
Authors: Seyedali Mirjalili, Jin Song Dong
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-24835-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-24834-5Published: 07 August 2019
eBook ISBN: 978-3-030-24835-2Published: 24 July 2019
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XI, 58
Number of Illustrations: 1 b/w illustrations, 25 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Operations Research/Decision Theory