A Self-adaptive Evolutionary Algorithm for Multi-objective Optimization

Authors:
Ruifen Cao;Guoli Li;Yican Wu
Affiliations:
Institute of Plasma Physics, Chinese Academy of Sciences, Hefei of Anhui. Prov., 230031, China;School of Electrical Engineering and Automation, Hefei, University of Technology, Hefei 230009, China;Institute of Plasma Physics, Chinese Academy of Sciences, Hefei of Anhui. Prov., 230031, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation,PcandPm, are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.