A μ-AEA Constraint Optimization Algorithm Based on AEA
WANG Zhen,LI Shao-Jun
Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education, East China University of Science and Technology,Shanghai 200237
Abstract：A constrained handling method based on the Alopex-based evolutionary algorithm(AEA) is proposed. The relatively feasible region is gradually converged to the feasible region by the introducing adaptive relaxation parameter μ in the iteration,which takes into account that different functions have different sizes of feasible regions. Also the relaxation of constraints allows more infeasible individuals which contain some useful information to keep staying in the next generation. And therefore it enhances search ability of the algorithm. At the same time,an adaptive penalty function method is introduced,and it adaptively adjusts the penalty coefficient based on the different constraint satisfactions. Thus,it ensures that the punishment is not too large or too small. 11 standard test function experiments show that the proposed method has satisfactory results and great potential in handling works with constraint optimization problems.
[1]Coello C A C. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: A Survey of the State of the Art .Computer Methods in Applied Mechanics and Engineering,2002,191(11/12): 1245-1287 [2] Deb K. An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering,2000,186(2/3/4): 311-338 [3] Lampinen J. A Constraint Handling Approach for the Differential Evolution Algorithm // Proc of the Congress on Evolutionary Computation. Honolulu,USA,2002: 1468-1473 [4] He Qie,Wang Ling. A Hybrid Particle Swarm Optimization with a Feasibility-Based Rule for Constrained Optimization. Applied Mathematics and Computation,2007,186(2): 1407-1422 [5] Takahama T,Sakai S. Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites // Proc of the IEEE Congress on Evolutionary Computation. Vancouver,Canada,2006: 1-8 [6] Li Shaojun. An Alopex Based Evolutionary Optimization Algorithms.Pattern Recognition and Artificial Intelligence,2009,22(3): 452-456 (in Chinese) (李绍军.一种基于Alopex的进化优化算法.模式识别与人工智能,2009,22(3): 452-456) [7］Zhang Haibo,Rangaiah G P. An Efficient Constraint Handling Method with Integrated Differential Evolution for Numerical and Engineering Optimization. Computers Chemical Engineering,2012,37: 74-88 [8] Mei Zhenzhen,Li Shaojun,Sang Zhixiang. Improved AEA and Its Application on Parameter Estimation of Heavy Oil Thermal Cracking Three Lumps Model. Computers and Applied Chemistry,2012,29(1): 71-74 (in Chinese) (梅真贞,李绍军,桑志祥.改进的AEA算法及其在重油热解模型参数估计中的应用.计算机应用与化学,2012,29(1): 71-74) [9］da Silva E K,Barbosa H J C,Lemonge A C C. An Adaptive Constraint Handling Technique for Differential Evolution in Engineering Optimization // Proc of the International Conference on Engineering Optimization. Rio de Janeiro,Brazil,2008: 1-10 [10]da Silva E K,Barbosa H J C,Lemonge A C C. An Adaptive Constraint Handling Technique for Differential Evolution with Dynamic Use of Variants in Engineering Optimization. Optimization and Engineering,2011,12(1/2): 31-54
2013, Vol. 26 
