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arXiv · 2011.09722

Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations

Felipe Vaz·Yuri Lavinas·Claus Aranha·Marcelo Ladeira
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Abstract

Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling Techniques (CHTs) on MOEA/D, a commonly used MOP solver when solving complex real-world MOPs. Moreover, we introduce a simple and effective CHT focusing on the exploration of the decision space, the Three Stage Penalty. We explore each of these CHTs in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The results of this work indicate that while the best CHT is problem-dependent, our new proposed Three Stage Penalty achieves competitive results and remarkable performance in terms of hypervolume values in the hard simulated car design MOP.

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Felipe Vaz, Yuri Lavinas, Claus Aranha, Marcelo Ladeira. 2020-11-19. Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations. https://arxiv.org/abs/2011.09722

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