Soft Computing & Data Mining Centre (SMC), Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia.
Institute of Computer Sciences and IT (ICS/IT), The University of Agriculture, Peshawar, Pakistan.
PLoS One. 2021 Aug 6;16(8):e0255269. doi: 10.1371/journal.pone.0255269. eCollection 2021.
The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
正弦余弦算法（SCA）是一种基于种群的启发式算法，利用正弦和余弦函数进行搜索。为了实现搜索过程，SCA 结合了几个搜索参数。但是，这些参数有时会使 SCA 的搜索容易受到局部极值的影响。为了解决这个问题，本文提出了一种新的多正弦余弦算法（MSCA）。MSCA 利用多个群体集群来分散和增强搜索，以避免局部极值问题。其次，在更新过程中，MSCA 还会检查更好的搜索集群，以有效地收敛到全局最小值。为了评估其性能，我们在单峰、多峰和复合基准函数上对 MSCA 进行了测试，这些函数取自文献。实验结果表明，与最近的一些最先进的启发式算法相比，包括原始 SCA 在内，MSCA 在收敛性方面具有统计学上的优势。
