An Adaptive Population-based Simplex Method for Continuous Optimization

作     者：Omran, Mahamed G. H. Clerc, Maurice 

作者机构：Gulf Univ Sci & Technol Dept Comp Sci Hawally Kuwait 

出 版 物：《INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH》 (国际群智能研究杂志)

年 卷 期：2016年第7卷第4期

页      面：23-51页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Kuwait Foundation for the Advancement of Science (KFAS) [P114-18SM-02] 

主　　题：Continuous Function Optimization Low-dimensional Simplex Evolution Nelder-Mead Simplex Population-based Optimization Methods Triangle Evolution 

摘      要：This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.