Differential evolution algorithm with dynamic population scheme
Xinyu Zhou, Mingwen Wang, Jianyi Wan · International Journal of Wireless and Mobile Computing · 2016
Differential evolution (DE) is an efficient population-based evolutionary algorithm for solving optimisation problems. Its control parameters have significant influence on performance. In the past few years, how to adjust the scaling factor F and crossover probability CR has attracted a lot of attention. However, few works have been focused on the parameter of population size. So in this paper, we designed a dynamic population scheme to manage the population size of DE. This scheme mainly consists of two parts: the logistic model and opposition-based learning. The first part is dedicated to calculate how many new individuals should be added into the population, while the second part is used to generate these new individuals. A series of experiments is conducted on 25 well-known benchmark functions including shifted and rotated ones. Results show that our approach shows promising performance.