2018/11/06 by Kantour Nedjmeddine, Nedjmeddine Kantour, Nedjmeddine, Kantour +6
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #cs.NE #math.OC
paper · pdf · doi:10.48550/arxiv.1811.02271
24 pages, 08 figures, 05 tables
arxiv created 2018/11/06 · openalex publication_date 2018/11/06 · arxiv updated 2018/11/07 · openalex created_date 2018/11/16 · openalex updated_date 2026/07/28
In this paper, we propose a parallel multiobjective evolutionary algorithm called Parallel Criterion-based Partitioning MOEA (PCPMOEA), with an application to the Mutliobjective Knapsack Problem (MOKP). The suggested search strategy is based on a periodic partitioning of potentially efficient solutions, which are distributed to multiple multiobjective evolutionary algorithms (MOEAs). Each MOEA is dedicated to a sole objective, in which it combines both criterion-based and dominance-based approaches. The suggested algorithm addresses two main sub-objectives: minimizing the distance between the current non-dominated solutions and the ideal point, and ensuring the spread of the potentially efficient solutions. Experimental results are included, where we assess the performance of the suggested algorithm against the above mentioned sub-objectives, compared with state-of-the-art results using well-known multi-objective metaheuristics.