Handling multi objective optimization problems with unbalanced constraints and their effects on evolutionary algorithm performance
Abstract Despite the successful application of an extension of the Multi Objective Evolution Algorithm based on Decomposition (MOEA/D M2M) to solve unbalanced multi objective optimization problems (UMOPs) its use in constrained unbalanced multi objective optimization problems has not been fully explored. In an earlier paper, a definition of UMOPs was suggested that had two necessary conditions: 1) finding a favored subset of the Pareto set is easier than finding an unfavored subset, and 2) the favored subset of the Pareto set dominates a large part of the feasible space. The second condition strongly reduces the fraction of MOPs that are considered UMOPs. In this paper, we eliminate that second condition and consider a broader class of UMOPs. We design an unbalanced constrained multi objective test suite with three different types of biased constraints, yielding three different types of constrained test problems in which the degree of imbalance is scalable via a set of parameters introduced for each problem. We analyse the characteristics of three types of constraints and the difficulties they present for potential solution algorithms i.e. NSGA II, MOEA/D and MOEA/D M2M, with four constraint handling techniques. MOEA/D M2M is shown to significantly outperform the other algorithms on these problems due to its decomposition strategy.
Swarm Evol. Comput.