一种混合策略的Pareto演化规划  

A Mixed Strategies Pareto Evolutionary Programming

作　　者：董红斌 黄厚宽[1] 何军[1] 侯薇 穆成坡[1] 

机构地区：[1]北京交通大学计算机与信息技术学院 [2]东北农业大学计算机科学系,哈尔滨150030

出　　处：《模式识别与人工智能》2006年第6期794-800,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60443003);黑龙江省自然科学基金(No.F200605)资助项目

摘　　要：提出一种多目标演化算法——混合策略Pareto演化规划(Mixed Strategies Pareto Evolutionary Programming,MSPEP).借鉴强度Pareto Ⅱ演化算法的个体比较技术,通过计算个体位序的Pareto强度值进行比较排序,混合策略变异机制用于指导算法有效搜索过程,标准测试函数的实验结果验证算法的通用性和有效性,算法搜索的解集能快速逼近Pareto最优前沿。A evolutionary approach to solve the muhiobjective optimization problems, Mixed Strategies Pareto Evolutionary Programming （MSPEP）, is presented. Based on the performance of mutation strategies, the mixed strategy distribution is dynamically adjusted. By combining the Pareto strength ranking procedure with the mixed mutation strategies, a new evolutionary algorithm is proposed. The proposed approach is compared with other evolutionary optimization techniques in several benchmark functions. Experimental results demonstrate that the proposed method could rapidly converge to the Pareto optimal front and spread widely along the front.

关 键 词：多目标优化 PARETO最优前沿 混合策略 演化规划 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]