基于指标和自适应边界选择的高维多目标优化算法

多目标优化算法的主要目标是实现好的多样性和收敛性.传统的高维多目标优化算法,当目标维数增加时,选择方式难以平衡种群的收敛性与多样性.对此,提出一个基于指标和自适应边界选择的高维多目标优化算法.在环境选择中,首先计算种群中两两个体的指标Iε(x,y)作为第一选择标准;其次,提出一种自适应边界选择策略,利用种群进化信息对超...

An indicator-based many-objective evolutionary algorithm with adaptive boundary selection

The main goal of the multi-objective optimization algorithm is to achieve good diversity and convergence. In traditional many-objective optimization algorithms, the selection operator is difficult to balance the convergence and diversity of the population, when the dimensionality of the objective increases. To solve this problem, this paper proposes a many-objective algorithm named an indicator-based many-objective evolutionary algorithm with adaptive boundary selection. In environmental selection, it first calculates the index $I_\varepsilon (x,y)$ of the two bodies in the population as the first selection criterion, and then proposes an adaptive boundary selection strategy, which uses population evolution information to make fuzzy predictions of hyperplane coefficients, and then approximately calculate the paradigm distance from the candidate individual to the hyperplane as the second selection criterion. Finally, the proposed algorithm is compared with five representative many-objective optimization algorithms. The experimental results show that when the algorithm handles many-objective optimization problem of the complex Pareto frontier, it can balance convergence and diversity while better maintaining diversity.