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Cooperative meta-heuristic algorithms for global optimization problems
Expert Systems with Applications ( IF 8.5 ) Pub Date : 2021-03-04 , DOI: 10.1016/j.eswa.2021.114788
Mohamed Abd Elaziz , Ahmed A. Ewees , Nabil Neggaz , Rehab Ali Ibrahim , Mohammed A.A. Al-qaness , Songfeng Lu

This paper presents an alternative global optimization meta-heuristics (MHs) approach, inspired by the natural selection theory. The proposed approach depends on the competition among six MHs that allows generating an offspring, which can breed the high characteristics of parents since they are unique and competitive. Therefore, this leads to improve the convergence of the solutions towards an optimal solution and also, to avoid the limitations of other methods that aim to balance between exploitation and exploration. The six algorithms are differential evolution, whale optimization algorithm, grey wolf optimization, symbiotic organisms search algorithm, sine–cosine algorithm, and salp swarm algorithm. According to these algorithms, three variants of the proposed method are developed, in the first variant, one of the six algorithms will be used to update the current individual based on a predefined order and the probability of the fitness function for each individual. Whereas, the second variant updates each individual by permuting the six algorithms, then using the algorithms in the current permutation to update individuals. The third variant is considered as an extension of the second variant, which updates all individuals using only one algorithm from the six algorithms. Three different experiments are carried out using CEC 2014 and CEC 2017 benchmark functions to evaluate the efficiency of the proposed approach. Moreover, the proposed approach is compared with well known MH methods, including the six methods used to build it. Comparison results confirmed the efficiency of the proposed approach compared to other approaches according to different performance measures.



中文翻译:

全局优化问题的协同元启发式算法

本文介绍了另一种受自然选择理论启发的全局优化元启发式(MH)方法。所提出的方法取决于六个MH的竞争,这些竞争允许产生后代,由于后代具有独特性和竞争力,因此可以孕育父母的高品质。因此,这导致将解决方案的收敛性提高到最佳解决方案,并且避免了其他旨在在开发和勘探之间取得平衡的方法的局限性。这六种算法分别是差分进化,鲸鱼优化算法,灰太狼优化,共生生物搜索算法,正弦余弦算法和小群算法。根据这些算法,开发了所提出方法的三个变体,在第一个变体中,六种算法中的一种将用于基于预定义的顺序和每个个体的适应度函数的概率来更新当前个体。而第二个变体通过置换六个算法来更新每个个体,然后使用当前置换中的算法来更新个体。第三变体被视为第二变体的扩展,第二变体仅使用六种算法中的一种算法来更新所有个体。使用CEC 2014和CEC 2017基准功能进行了三个不同的实验,以评估所提出方法的效率。此外,将所提出的方法与众所周知的MH方法(包括用于构建它的六种方法)进行了比较。

更新日期:2021-04-04
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