ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment
ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment
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DOI：
10.1016/j.eswa.2018.01.019
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发表时间：
2018-06-01
影响因子：
8.5
通讯作者：
Alzohairy, Ahmed
中科院分区：
文献类型：
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作者：
Issa, Mohamed;Hassanien, Aboul Ella;Alzohairy, Ahmed
正弦余弦算法（SCA），最近提出的人口为基础的优化算法，是基于使用正弦和余弦三角函数作为运营商更新的运动的搜索代理。为了优化性能，必须适当地调优SCA上的不同参数。设置这样的参数是具有挑战性的，因为它们允许算法从局部最优值逃脱，避免过早收敛。SCA的主要缺点是参数设置只影响突出区域的利用。然而，SCA具有良好的探索能力。本文通过将SCA与粒子群优化（PSO）合并，提供了SCA的增强版本。PSO比标准SCA的运算符更好地利用了搜索空间。所提出的算法，称为ASCA-PSO，已经过测试，在几个单峰和多峰的基准函数，这表明其优越性SCA和其他最近和标准的元启发式算法。此外，为了验证SCA的能力，SCA已被用于解决成对局部比对算法的现实问题，该算法倾向于找到两个生物序列之间的最长连续子串。实验结果证明了ASCA-PSO解决方案在精度和计算时间方面的良好性能。(C)2018爱思唯尔有限公司版权所有。
The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time. (C) 2018 Elsevier Ltd. All rights reserved.