Differential evolution using multi-strategy for the improvement of optimization performance - Liu, Nengxian; Luo, Jianbin; Chang, Jie; Pan, Jeng-Shyang (Neural computing & applications, 2025)
Differential evolution (DE) is an effective population-based optimization approach that has been widely used to deal with scientific and engineering problems. However, the performance of DE method is largely dependent on its trial vector produce strategy, namely, mutation strategy, crossover operation and its corresponding control parameters. As claimed by the ‘No free Lunch theorem’, each mutation or crossover strategy has its fatal flaws; hence, the DE method having a single operation strategy cannot solve all types of optimization problems. Therefore, we propose a novel multi-strategy DE (MS-DE) in this study. First, the proposed algorithm uses combined mutation strategies including two powerful mutation strategies and selects them in a probabilistic way. Second, an improved crossover operation is introduced to tackle the stagnation problem. When a stagnation occurs, DE employs the top p-best vector to conduct crossover operation. Third, the control parameters are tuned in novel adaptation schemes. Finally, a local search is utilized in the proposed method to accelerate the convergence. The proposed MS-DE method is examined on CEC2017 test suite, and experiment results confirm its outperformance over several state-of-the-art DE methods. Furthermore, the proposed MS-DE is applied to two constrained engineering problems. The comparison results on these two problems also demonstrate the efficiency of our proposed MS-DE.