约束进化算法及其应用研究综述

Survey of Constrained Evolutionary Algorithms and Their Applications

约束优化问题广泛存在于科学研究和工程实践中,其对应的约束优化进化算法也成为了进化领域的重要研究方向。约束优化进化算法的本质问题是如何有效地利用不可行解和可行解的信息,平衡目标函数和约束条件,使得算法更加高效。首先对约束优化问题进行定义;然后详细分析了目前主流的约束进化算法,同时,基于不同的约束处理机制,将这些机制分为约束和目标分离法、惩罚函数法、多目标优化法、混合法和其他算法,并对这些方法进行了详细的分析和总结;接着指出约束进化算法亟待解决的问题,并明确指出未来需要进一步研究的方向;最后对约束进化算法在工程优化、电子和通信工程、机械设计、环境资源配置、科研领域和管理分配等方面的应用进行了介绍。

Constrained optimization problems exist widely in scientific research and engineering practice,and the corresponding constrained evolutionary algorithms have become an important research direction in the field of evolutionary computation.The essential problem of constrained evolutionary algorithm is how to effectively use the information of infeasible and feasible solutions and balance the objective function and constraints to make the algorithm more efficient.Firstly,this paper defines the problem of constraint optimization.Then it analyzes the current mainstream constraint evolution algorithms in detail.At the same time,based on different constraint handling mechanisms,these mechanisms are divided into constraint and objective separation methods,pena-lty function methods,multi-objective optimization methods,hybrid methods and so on,and these methods are analyzed and summarized comprehensively.Next,it points out the urgent problems that need to be solved as well as the research direction.Finally,the application of constrained evolutionary algorithm in engineering optimization,electronic and communication engineering,mechanical design,environmental resource allocation,scientific research and management allocation are introduced.