Evaluation of the migrated solutions for distributing reference point-based multi-objective optimization algorithms

As the number of objectives and/or dimension of a given problem increases, or a real-world optimization problem is modeled in more detail, the optimization algorithm requires more computation time if the computational resources are fixed. Therefore, some more tools are needed to be developed for deployment of these resources. The parallelization is one of these tools based on distribution of the overall problem to different computational units. In this study, a distributed computing approach for multi-objective evolutionary optimization algorithms is proposed by application of a migration policy which is based on sharing the information for inter-processor collaboration. This idea is also intensified with the crossover operator at the evolutionary algorithms where the migrated solutions are applied to the crossover operator so that the performance of the overall approach increases. Besides, a new metric is defined for evaluation of the performance of the proposed distribution methodology. The performance of the proposed approaches is evaluated via well-known two- and three-objective well-known test problems.