The Adaptive Population-based Simplex method

Abstract

A novel, tuning-free, population-based simplex method for continuous function optimization is proposed. The proposed method, called Adaptive Population-based Simplex (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. The approach is easy to code and easy to understand. APS is compared with four state-of-the-art approaches on five real-world problems. The experimental results show that APS generally performs better than the other methods on the test problems.