一种基于AEA的约束优化算法μ-AEA  

A μ-AEA Constraint Optimization Algorithm Based on AEA

作　　者：王振[1] 李绍军[1] 

机构地区：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室上海200237

出　　处：《模式识别与人工智能》2013年第9期859-864,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金资助项目(No.20976048,21176072)

摘　　要：提出一种基于AEA算法的约束处理方法,该方法通过引入在迭代中自适应调整的松弛参数μ,逐渐缩小相对可行域直至收敛到可行域,且充分考虑到不同函数具有不同的可行域大小的情况.松弛约束的引入能允许包含有用信息的不可行解进入到子代种群中,增加算法的搜索能力.同时,引入一种自适应惩罚函数法,它利用不同约束条件满足的难易程度来自适应地调整惩罚系数,保证惩罚力度不会过大或过小.通过11个标准测试函数实验表明,该方法具有较满意的结果,在处理工程约束优化问题方面具有很大的潜力.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.

关 键 词：AEA算法 约束优化问题 惩罚函数法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O224[自动化与计算机技术—控制科学与工程]