An Improved Version of Volume Dominance for Multi-Objective Optimisation

Authors:
Khoi Le;Dario Landa-Silva;Hui Li
Affiliations:
Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, The University of Nottingham,;Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, The University of Nottingham,;Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, The University of Nottingham,
Venue:
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Year:
2009

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Abstract

This paper proposes an improved version of volume dominance to assign fitness to solutions in Pareto-based multi-objective optimisation. The impact of this revised volume dominance on the performance of multi-objective evolutionary algorithms is investigated by incorporating it into three approaches, namely SEAMO2, SPEA2 and NSGA2 to solve instances of the 2-, 3- and 4- objective knapsack problem. The improved volume dominance is compared to its previous version and also to the conventional Pareto dominance. It is shown that the proposed improved volume dominance helps the three algorithms to obtain better non-dominated fronts than those obtained when the two other forms of dominance are used.