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Multi-Objective Optimization using Artificial Intelligence Techniques / by Seyedali Mirjalili, Jin Song Dong.
Springer Nature - Springer Intelligent Technologies and Robotics eBooks 2020 English International Available online
View online- Format:
- Book
- Author/Creator:
- Mirjalili, Seyedali., Author.
- Dong, Jin Song., Author.
- Series:
- SpringerBriefs in Computational Intelligence, 2625-3712
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Machine learning.
- Operations research.
- Computational Intelligence.
- Machine Learning.
- Operations Research and Decision Theory.
- Local Subjects:
- Computational Intelligence.
- Machine Learning.
- Operations Research and Decision Theory.
- Physical Description:
- 1 online resource (xi, 58 pages)
- Edition:
- 1st ed. 2020.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 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.
- ISBN:
- 3-030-24835-6
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