© 2020

Multi-Objective Optimization using Artificial Intelligence Techniques


Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Seyedali Mirjalili, Jin Song Dong
    Pages 1-9
  3. Seyedali Mirjalili, Jin Song Dong
    Pages 11-20
  4. Seyedali Mirjalili, Jin Song Dong
    Pages 21-36
  5. Seyedali Mirjalili, Jin Song Dong
    Pages 37-45
  6. Seyedali Mirjalili, Jin Song Dong
    Pages 47-58

About this book


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

Authors and affiliations

  1. 1.Torrens University AustraliaFortitude Valley, BrisbaneAustralia
  2. 2.Institute for Integrated and Intelligent SystemsGriffith UniversityBrisbaneAustralia

Bibliographic information