A Parallel MOEA with Criterion-based Selection Applied to the Knapsack Problem
This is an incremental improvement for optimization researchers, addressing computational efficiency in multiobjective knapsack problems.
The paper tackles the Multiobjective Knapsack Problem by proposing a parallel evolutionary algorithm that partitions solutions and uses criterion-based selection, achieving competitive performance against state-of-the-art methods in minimizing distance to the ideal point and ensuring solution spread.
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.