Differential evolution with ring sub-population architecture for optimization

Abstract

In recent years, evolutionary algorithms have achieved outstanding results in addressing increasingly complex optimization problems, with differential evolution (DE) gaining significant attention. However, due to its simple yet efficient evolutionary mechanism, DE has consistently faced challenges in mitigating the risk of premature convergence. This paper introduces a novel Ring Sub-population architecture-based Differential Evolution (RSDE) to address this issue. RSDE incorporates a conditional similarity selection mechanism that integrates multiple strategies. By considering fitness evaluation and population distribution, RSDE facilitates rich information exchange among sub-populations, leading to cyclic optimization. This global conditional interaction mechanism provides a new idea for population structure research, effectively preserves valuable solutions within the population, and prevents stagnation due to rapid convergence. The performance of RSDE is rigorously evaluated using 29 benchmark functions from the IEEE Congress on Evolutionary Computation (CEC) 2017, 22 real-world problems from CEC2011, and 12 complex optimization problems from CEC2022. RSDE is compared with 18 advanced algorithms, including leading DE variants and other state-of-the-art methods. The results demonstrate that the proposed RSDE algorithm performs well and is highly competitive with other competitors.