Evolutionary multi-objective optimisation by diversity control

Authors:
Pasan Kulvanit;Theera Piroonratana;Nachol Chaiyaratana;Djitt Laowattana
Affiliations:
Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;Research and Development Center for Intelligent Systems, King Mongkut's Institute of Technology North Bangkok, Bangkok, Thailand;Research and Development Center for Intelligent Systems, King Mongkut's Institute of Technology North Bangkok, Bangkok, Thailand;Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Venue:
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Year:
2006

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Abstract

This paper presents an improved multi-objective diversity control oriented genetic algorithm (MODCGA-II). The performance comparison between the MODCGA-II, a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto evolutionary algorithm (SPEA-II) is carried out where different two- and three-objective benchmark problems with specific multi-objective characteristics are used. The results indicate that the two-objective MODCGA-II solutions are better than the solutions generated by the NSGA-II and SPEA-II in terms of the closeness to the true Pareto optimal solutions and the uniformity of solution distribution along the Pareto front. In contrast, the NSGA-II in overall produces the best solutions in three-objective problems. As a result, the limitation of the proposed algorithm is identified.