# 一种求解约束优化问题基于混合遗传算子的遗传算法A Genetic Algorithm Based on Hybrid Genetic Operators for Solving Constrained Optimization Problems

DOI: 10.12677/ORF.2014.41001, PDF, HTML, 下载: 2,247  浏览: 6,912  国家自然科学基金支持

>For the case that the optima of constrained optimization problems are often located on boundary of the feasible region, a new genetic algorithm based on hybrid genetic operators is proposed in this paper. First, in this algorithm, the crossover is executed according to feasible and infeasible individuals, respectively. A feasible point is always combined with the best-known one found so far for crossover, whereas an infeasible individual is selected according to the fitness for crossover with any feasible one. In addition, in order to make infeasible solutions become feasible ones and make feasible points move toward the boundary of feasible region, a hybrid mutation operator is presented based on boundary mutation and Gaussian mutation. Numerical experiments and comparison results show the efficiency of the method.

 [1] Runarsson H. P. and Yao X. (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4, 284-294. [2] Venkatraman, S. and Yen, G.G. (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Transactions on Evolutionary Computation, 9, 424-435. [3] Michalewicz, Z. and Schoenauer, M. (1996) Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Commputation, 4, 1-32. [4] 梁昔明, 秦浩宇, 龙文 (2010) 一种求解约束优化问题的遗传算法. 计算机工程, 14, 147-149. [5] 梁昔明, 龙文, 秦浩宇等 (2010) 基于种群个体可行性的约束优化进化算法. 控制与决策, 8, 1129-1138. [6] Farmani, R. and Wright, J.A. (2003) Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 5, 445-455. [7] 范小勤, 汪小红, 尹洁 (2010) 约束优化问题的改进混合遗传算法. 过程控制, 7, 13-16. [8] 刘大莲, 徐尚文 (2012) 求解约束优化问题的内外交叉遗传算法. 系统工程与实践, 1, 189-195. [9] Mezura-Montes, E. and Coello Coello, C.A. (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 9, 1-17. [10] Kelner, V., Capitanescu, F., Leonardand, O., et al. (2008) A hybrid optimization technique coupling an evolutionary and a local search algorithm. Journal of Computational and Applied Mathematics, 215, 448-456. [11] Wang, Y., Cai, Z.X., Zhou, Y.R., et al. (2008) An adaptive trade-off Model for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 12, 80-92. [12] Deb, K. (2005) A population algorithm genertor for real-parameter optimization. Soft computing—A foundations. Methodologies and Applications, 9, 236-253. [13] Rouarsson, T.P. and Yao, X. (2005) Search biases in constrained evolutionary optimization. IEEE Transactions on System, Man and Cybernetics, 35, 233-243. [14] 张创业, 何登旭, 莫愿斌 (2010)多群多层协同进化算法的约束优化求解及应用. 计算机应用研究, 5, 1638-1647.