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.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.
A hybrid algorithm （Powell-IEA） based on the Powell search method and immune evolutionary algorithm for unconstrained optimization is proposed. 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. 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 has demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for unconstrained optimization.
出处
《江西师范大学学报（自然科学版）》
CAS
北大核心
2010年第1期53-56,共4页
Journal of Jiangxi Normal University(Natural Science Edition)
基金
国家自然科学基金(50771052)资助项目
关键词
POWELL搜索法
免疫进化算法
无约束最优化
Powell search method
immune evolutionary algorithm
unconstrained optimization
