Ranking-Based Differential Evolution for Large-Scale Continuous Optimization

Abstract

Large-scale continuous optimization has gained considerable attention in recent years. Differential evolution (DE) is a simple yet efficient global numerical optimization algorithm, which has been successfully used in diverse fields. Generally, the vectors in the DE mutation operators are chosen randomly from the population. In this paper, we employ the ranking-based mutation operators for the DE algorithm to improve DE's performance. In the ranking-based mutation operators, the vectors are selected according to their rankings in the current population. The ranking-based mutation operators are general, and they are integrated into the original DE algorithm, GODE, and GaDE to verify the enhanced performance. Experiments have been conducted on the large-scale continuous optimization problems. The results indicate that the ranking-based mutation operators are able to enhance the overall performance of DE, GODE, and GaDE in the large-scale continuous optimization problems.