  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.
