Abstract

The Sine Cosine Algorithm (SCA) is a recently developed efficient metaheuristic algorithm to find the solution of global optimization problems. However, in some circumstances, this algorithm suffers the problem of low exploitation, skipping of true solutions and insufficient balance between exploration and exploitation. Therefore, the present paper aims to alleviate these issues from SCA by proposing an improved variant of SCA called HSCA. The HSCA modifies the search mechanism of classical SCA by including the leading guidance and hybridizing with simulated quenching algorithm. The proposed HSCA is tested on classical benchmark set, standard and complex benchmarks sets IEEE CEC 2014 and CEC 2017 and four engineering optimization problems. In addition to these problems, the HSCA is also used to train multilayer perceptrons as a real-life application. The experimental results and analysis on benchmark problems and real-life application problems demonstrate the superiority of the HSCA as compared to other comparative optimization algorithms.

中文翻译：

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一种新的全局优化混合正弦余弦算法及其在训练多层感知器中的应用

摘要

正弦余弦算法（SCA）是最近开发的一种有效的元启发式算法，用于查找全局优化问题的解决方案。但是，在某些情况下，该算法存在开发利用率低，跳过真实解法以及勘探与开发之间的平衡不足的问题。因此，本文旨在通过提出一种称为HSCA的SCA改进版本来缓解SCA的这些问题。HSCA通过包括领先的指导并与模拟淬灭算法混合来修改经典SCA的搜索机制。提议的HSCA在经典基准测试集，标准和复杂基准测试集IEEE CEC 2014和CEC 2017以及四个工程优化问题上进行了测试。除了这些问题，HSCA还用于训练多层感知器，作为现实生活中的应用。与其他比较优化算法相比，对基准问题和实际应用问题的实验结果和分析证明了HSCA的优越性。