Chakraborty Sanjoy, Saha Apu Kumar, Sharma Sushmita, Sahoo Saroj Kumar, Pal Gautam
Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura 799046 India.
Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura 799155 India.
J Bionic Eng. 2022;19(4):1140-1160. doi: 10.1007/s42235-022-00190-4. Epub 2022 Jun 13.
Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE) is one of the most successful algorithms and is frequently used to solve various industrial problems. Over the previous 2 decades, DE has been heavily modified to improve its capabilities. Several DE variations secured positions in IEEE CEC competitions, establishing their efficacy. However, to our knowledge, there has never been a comparison of performance across various CEC-winning DE versions, which could aid in determining which is the most successful. In this study, the performance of DE and its eight other IEEE CEC competition-winning variants are compared. First, the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions, and the performances have been compared. One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems, out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite, have been solved to compare the performances. Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm's ability statistically. Performance analysis exposes that none of the DE variants can solve all the problems efficiently. Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.
由于其卓越的问题解决能力，受自然启发的优化算法正被广泛用于解决复杂的现实世界优化问题。工程学界最近聚焦于元启发式算法来应对各种优化挑战。在众多先进算法中，差分进化（DE）是最成功的算法之一，常被用于解决各种工业问题。在过去的20年里，DE被大量改进以提升其性能。一些DE变体在IEEE CEC竞赛中名列前茅，证明了它们的有效性。然而，据我们所知，从未对各种CEC获奖的DE版本的性能进行过比较，而这有助于确定哪一个是最成功的。在本研究中，对DE及其其他八个IEEE CEC竞赛获奖变体的性能进行了比较。首先，对这些算法进行了IEEE CEC 2019和2020边界约束函数的评估，并比较了性能。解决了IEEE CEC 2011问题套件中的一个无约束问题以及其他五个约束机械工程设计问题，其中四个问题取自IEEE CEC 2020非凸约束优化套件，以比较性能。执行了诸如弗里德曼检验和威尔科克森检验等统计分析，以从统计学上验证算法的能力。性能分析表明，没有一个DE变体能够高效地解决所有问题。在用于解决此类机械设计问题的比较方法中，SHADE和ELSHADE-SPACMA的性能相当可观。
