基于环境Pareto 支配选择策略的有约束多目标差分进化算法

在处理有约束多目标问题的进化算法中, 目前普遍采用Deb 教授提出的约束占优的直接支配选择策略. 在约束处理中, 优秀不可行解与优秀可行解同样重要, 但在直接支配选择策略中, 不可行解被选择的几率很小. 针对此问题, 设计一种环境Pareto 支配的选择策略, 并基于此提出用于解决有约束多目标问题的差分进化算法. 对经典测试函数进行仿真计算, 结果表明, 与其他算法相比, 所提出的算法具有更高的收敛性和稳定性.

Differential evolution algorithm based on environment Pareto dominated selection strategy in constrained multi-objective optimization problem

In the evolutionary algorithm with constrained multi-objective problems, the selection strategy of constraineddominate proposed by Professor Deb is widely used. The excellent infeasible solution is equally important as the feasible solution in the constraint treatment method. The infeasible solution has a small chance of winning in the selection strategy of constrained-dominate. Therefore, a differential evolution algorithm based on the environment pareto dominated selection strategy in the constrained multi-objective optimization problem is proposed. Benchmark functions are simulated, and the results show that, compared with other algorithms, the proposed algorithm has better convergence and stability.