An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems
This work addresses optimization problems in evolutionary computation, but it is incremental as it builds on existing differential evolution variants.
The authors tackled the challenge of solving complex single-objective bound-constrained optimization problems by proposing a reconstructed differential evolution (RDE) algorithm that recombines effective strategies from recent variants, and the results show it achieves superior performance on the CEC2024 benchmark suite.
Complex single-objective bounded problems are often difficult to solve. In evolutionary computation methods, since the proposal of differential evolution algorithm in 1997, it has been widely studied and developed due to its simplicity and efficiency. These developments include various adaptive strategies, operator improvements, and the introduction of other search methods. After 2014, research based on LSHADE has also been widely studied by researchers. However, although recently proposed improvement strategies have shown superiority over their previous generation's first performance, adding all new strategies may not necessarily bring the strongest performance. Therefore, we recombine some effective advances based on advanced differential evolution variants in recent years and finally determine an effective combination scheme to further promote the performance of differential evolution. In this paper, we propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems. Based on the benchmark suite of the 2024 IEEE Congress on Evolutionary Computation (CEC2024), we tested RDE and several other advanced differential evolution variants. The experimental results show that RDE has superior performance in solving complex optimization problems.