约束优化进化算法综述

Overview of Constrained Optimization Evolutionary Algorithms

约束优化进化算法主要研究如何利用进化计算方法求解约束优化问题,是进化计算领城的一个重要研究课题.约束优化问题求解存在约束区域离散、等式约束、非线性约束等挑战,其问题的本质是,如何处理可行解与不可行解的关系才能使得算法更高效.首先介绍了约束优化问题的定义;然后,系统地分析了目前存在的约束优化方法;同时,基于约束处理机制,将这些方法分为罚函数法、可行性法则、随机排序法、?-约束处理法、多目标优化法、混合法等6类,并从约束处理方法的角度对约束优化进化算法的最新研究进展进行综述;最后,指出约束优化进化算法需进一步研究的方向与关键问题.

Constrained optimization evolutionary algorithm, which mainly studies how to use evolutionary computation method to solve constrained optimization problems, is an important research topic in evolutionary computation field. Discrete constraint, equality constraint, nonlinear constraints are challenges to solving constraint optimization. The basis of this problem solving is how to handle the relationship between feasible solution and infeasible solution. In this study, the definition of constrained optimization problem is firstly provided, and then, the existing constrained optimization approaches are systematically analyzed. Meanwhile, algorithms are classified into six categories （i.e., penalty function method, feasible rules, stochastic ranking, 6-constraint, multi-objective constraint handling, and hybrid method）, and the state-of-art constrained optimization evolutionary algorithms （COEAs） are surveyed with respect to constraint-handling techniques. Research progress and challenges of the six categories of constraint handling techniques are discussed in detail. Finally, the issues and research directions of constraint handling techniques are discussed.