A Parallel Evolutionary System for Multi-objective Optimisation
Mohammad Hamdan, Günter Rudolph, Nicola Hochstrate · 2020
Parallel evolutionary algorithms have been used for solving multiobjective optimization problems. The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. Also, superlinear speedup results were achieved for all test functions.