An Adaptive Population-Based Simplex Method for Heterogeneous Optimization

A new population-based simplex method for heterogeneous 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 hypersphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS will be tested on several benchmark functions and some real-world problems. It will be compared against state-of-the-art methods. In the second stage, we will investigate ways to enhance the performance of APS and to tackle real-world engineering problems