An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems
CoRR(2024)
摘要
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.
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