An exact penalty function-based differential search algorithm for constrained global optimization
An exact penalty function-based differential search algorithm for constrained global optimization
复制标题
基于精确罚函数的约束全局优化差分搜索算法
DOI：
10.1007/s00500-015-1588-6
复制
发表时间：
2016-04-01
期刊：
影响因子：
4.1
通讯作者：
Wu, Changzhi
中科院分区：
文献类型：
--
作者：
Liu, Jianjun;Teo, K. L.;Wu, Changzhi
差异搜索（DS）是一种最近开发的无衍生化全局启发式优化算法，用于解决无约束的优化问题。在本文中，通过应用确切的惩罚函数方法的概念，DS算法在其中引入了S型动力惩罚因素，以便在探索和开发之间取得更好的平衡，以解决约束的全球优化问题。为了说明拟议方法的适用性和有效性，通过将所提出的算法和其他广泛使用的进化方法应用于24个基准问题，进行了比较研究。获得的结果清楚地表明，对于大多数这些基准问题，所提出的方法比其他广泛使用的进化方法更有效。
Differential search (DS) is a recently developed derivative-free global heuristic optimization algorithm for solving unconstrained optimization problems. In this paper, by applying the idea of exact penalty function approach, a DS algorithm, where an S-type dynamical penalty factor is introduced so as to achieve a better balance between exploration and exploitation, is developed for constrained global optimization problems. To illustrate the applicability and effectiveness of the proposed approach, a comparison study is carried out by applying the proposed algorithm and other widely used evolutionary methods on 24 benchmark problems. The results obtained clearly indicate that the proposed method is more effective and efficient over the other widely used evolutionary methods for most these benchmark problems.