A new method to construct the non-dominated set in multi-objective genetic algorithms

Authors:
Jinhua Zheng;Zhongzhi Shi;Charles X. Ling;Yong Xie
Affiliations:
College of Information Engineering, Xiangtan University, Hunan, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Computer Science, University of Western Ontario, London, Canada;College of Information Engineering, Xiangtan University, Hunan, China
Venue:
Intelligent information processing II
Year:
2004

Citing 7
Cited 0

Genetic algorithms with sharing for multimodal function optimization

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Some Guidelines for Genetic Algorithms with Penalty Functions

Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method

Selected Papers from AISB Workshop on Evolutionary Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

There have been widespread applications for Multi Objective Genetic Algorithm (MOGA) on highly complicated optimization tasks in discontinuous, multi-modal, and noisy domains. Because the convergence of MOGA can be reached with the non-dominated set approximating the Pareto Optimal front, it is very important to construct the non-dominated set of MOGA efficiently. This paper proposes a new method called Dealer's Principle to construct non-dominated sets of MOGA, and the time complexity is analyzed. Then we design a new MOGA with the Dealer's Principle and a clustering algorithm based on the core distance of clusters to keep the diversity of solutions. We show that our algorithm is more efficient than the previous algorithms, and that it produces a wide variety of solutions. We also discuss the convergence and the diversity of our MOGA in experiments with benchmark optimization problems of three objectives.