2020/11/19 by F. A. Vaz, Yuri Lavinas, Vaz, Felipe +5
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Optimization and Mathematical Programming #Process Optimization and Integration
paper · pdf · doi:10.48550/arxiv.2011.09722
openalex publication_date 2020/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Finding good solutions for Multi-objective Optimization (MOPs) Problems is\nconsidered a hard problem, especially when considering MOPs with constraints.\nThus, most of the works in the context of MOPs do not explore in-depth how\ndifferent constraints affect the performance of MOP solvers. Here, we focus on\nexploring the effects of different Constraint Handling Techniques (CHTs) on\nMOEA/D, a commonly used MOP solver when solving complex real-world MOPs.\nMoreover, we introduce a simple and effective CHT focusing on the exploration\nof the decision space, the Three Stage Penalty. We explore each of these CHTs\nin MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The\nresults of this work indicate that while the best CHT is problem-dependent, our\nnew proposed Three Stage Penalty achieves competitive results and remarkable\nperformance in terms of hypervolume values in the hard simulated car design\nMOP.\n