Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

作者：Yue, Xuezhi^*; Guo, Zhaolu; Yin, Yali; Liu, Xiaosheng

来源：Soft Computing, 2018, 22(1): 137-146.

DOI：10.1007/s00500-016-2314-8

• Many-objective
• Dynamical evolutionary algorithm
• Adaptive-grid
• E-dominance

In evolutionary multi-objective optimization, achieving a balance between convergence speed and population diversity remains a challenging topic especially for many-objective optimization problems (MaOPs). To accelerate convergence toward the Pareto front and maintain a high degree of diversity for MaOPs, we propose a new many-objective dynamical evolutionary algorithm based on E-dominance and adaptive-grid strategies (EDAGEA). In EDAGEA, it incorporates the E_dominance and adaptive strategies to enhance the search ability. Instead of the Pareto dominance mechanism in the traditional dynamical evolutionary algorithm, EDAGEA employs the E-dominance strategy to improve the selective pressure and to accelerate the convergence speed. Moreover, EDAGEA incorporates the adaptive-grid strategy to promote the uniformity and diversity of the population. In the experiments, the proposed EDAGEA algorithm is tested on DTLZ series problems under 3-8 objectives with diverse characteristics and is compared with two excellent many-objective evolutionary algorithms. Experimental results demonstrate that the proposed EDAGEA algorithm exhibits competitive performance in terms of both convergence speed and diversity of population.

• 出版日期2018-1
• 单位江西理工大学