Center-Based Sampling for Population-Based Algorithms

Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES), are commonly used approaches to solve complex problems from science and engineering. They work with a population of candidate solutions. In this paper, a novel center-based sampling is proposed for these algorithms. Reducing the number of function evaluations to tackle with high-dimensional problems is a worthwhile attempt; the center-based sampling can open a new research area in this direction. Our simulation results confirm that this sampling, which can be utilized during population initialization and/or generating successive generations, could be valuable in solving large-scale problems efficiently. Quasi-Oppositional Differential Evolution is briefly discussed as an evidence to support the proposed sampling theory. Furthermore, opposition-based sampling and center-based sampling are compared in this paper. Black-box optimization is considered in this paper and all details about the conducted simulations are provided.