Multi-Objective Optimization using Artificial Intelligence Techniques

1. Front Matter

   Pages i-xi

   PDF

2. Introduction to Multi-objective Optimization

   • Seyedali Mirjalili, Jin Song Dong

   Pages 1-9

3. What is Really Multi-objective Optimization?

   • Seyedali Mirjalili, Jin Song Dong

   Pages 11-20

4. Multi-objective Particle Swarm Optimization

   • Seyedali Mirjalili, Jin Song Dong

   Pages 21-36

5. Non-dominated Sorting Genetic Algorithm

   • Seyedali Mirjalili, Jin Song Dong

   Pages 37-45

6. Multi-objective Grey Wolf Optimizer

   • Seyedali Mirjalili, Jin Song Dong

   Pages 47-58

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.

• 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

• Torrens University Australia, Fortitude Valley, Brisbane, Australia

  Seyedali Mirjalili

• Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

  Jin Song Dong

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