一个与Powell搜索相结合的混合免疫进化算法  

A Hybrid Powell Search And Immune Evolutionary Algorithm For Unconstrained Optimization

作　　者：刘国志[1] 苗晨[1] 杜翼辰[2] 

机构地区：[1]辽宁石油化工大学理学院信息与计算科学系,抚顺113001 [2]中山大学数学与计算科学学院,数学与应用数学专业中山510006

出　　处：《长春理工大学学报（自然科学版）》2010年第2期121-124,共4页Journal of Changchun University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金(50771052)

摘　　要：提出一个求解无约束最优化问题的新的混合算法-Powell搜索法和免疫进化算法的混合算法。该算法不需要计算梯度,容易应用于实际问题中。通过对免疫进化算法的修正,使混合算法具有更加精确和快速的收敛性。本文主要目的是通过加入混合策略说明免疫进化算法是能够被改进的。利用4个基准测试函数进行仿真计算比较,结果表明新混合算法在解的搜索质量、效率和关于初始点的鲁棒性都远优于免疫进化算法。仿真结果表明了新算法是求解无约束最优化问题的一个高效的算法。This paper proposes a hybrid algorithm （Powell-IEA） based on the Powell search method and immune evolutionary algorithm for unconstrained optimization.Powell-IEA is very easy to implement in practice since it does not require gradient computation.The modification of both the Powell search method and immune evolutionary algorithm intends to produce faster and more accurate convergence.The main purpose of the paper is to demonstrate how the immune evolutionary algorithm can be improved by incorporating a hybrid strategy.In a suit of 4 test function problems taken from the literature,the comparison report still largely favors the Powell-IEA algorithm in the performance of accuracy,robustness and function evaluation.As evidenced by the overall assessment based on computational experience,the new algorithm demonstrates to be extremely effective and efficient at locating best-practice optimal solutions for unconstrained optimization.

关 键 词：POWELL搜索法 免疫进化算法 无约束最优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]