A hypervolume distribution entropy guided computation resource allocation mechanism for the multiobjective evolutionary algorithm based on decomposition

The computation resource allocation is a key issue to the multiobjective evolutionary algorithms. Present studies still have some difficulties addressing this issue such as low accuracy, indispensable parameter for balancing the convergence and diversity, discontinuous improvement values of different domination relations and poor discernibility. To solve these problems, a hypervolume distribution model regarding two non-adjacent individuals of each subproblem is proposed in this paper. Based on the hypervolume distribution model, a comprehensive hypervolume distribution entropy (HDE) by integrating the entropy and the Kullback-Leibler divergence is proposed to measure the improvement of the subproblems. The proposed measurement is continuous over different domination relations, requires no parameters and has high precision and discernibility. Thereafter, a hypervolume distribution entropy guided multiobjective evolutionary based on decomposition (HDE-MOEA/D) is proposed. The proposed algorithm is more efficient on solving multiobjective optimization problems. The proposed algorithm is tested on some popular test suits against another five typical and popular algorithms. The proposed HDE-MOEA/D achieves the best generational distances and the inverse generational distances in 57.9% ranking comparisons. The HDE-MOEA/D also outperforms another compared algorithm in 70.2% one-to-one comparisons. The experiment results prove the superiority of the proposed algorithm and reveal some important discoveries. (C) 2021 Elsevier B.V. All rights reserved.