A Mutli-objective Evolutionary Algorithm with Adaptive Parallel Region Decomposition

Decomposition-based evolutionary multiobjective algorithms achieve good performance for solving the problems with regular Pareto fronts. Nevertheless, the shape of the Pareto front greatly influences the performance of the algorithms. Thus, we propose a new adaptive parallel region decomposition strategy. Different from the traditional decomposition-based methods, the proposed algorithm decomposes a multiobjective optimization problem into a number of subproblems by different ideal points, but not by different weight vectors. We compare the proposed algorithm with four state-of-the-art algorithms on seven test problems with irregular Pareto fronts. Experimental results show that the proposed algorithm has superior robustness on the optimization problems with irregular Pareto fronts.