An improved Opposition-Based Sine Cosine Algorithm for global optimization
An improved Opposition-Based Sine Cosine Algorithm for global optimization
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DOI：
10.1016/j.eswa.2017.07.043
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发表时间：
2017-12-30
影响因子：
8.5
通讯作者：
Xiong, Shengwu
中科院分区：
文献类型：
--
作者：
Abd Elaziz, Mohamed;Oliva, Diego;Xiong, Shengwu
现实生活中的优化问题需要适当探索搜索空间以获得最佳解的技术。从这个意义上说，传统的优化算法在局部最优值方面失败是很常见的。正弦余弦算法(SCA)是最近提出的一种基于两个三角函数的全局优化算法。SCA使用正弦和余弦函数来修改一组候选解；这样的操作符在探索和利用搜索空间之间创造了平衡。然而，与其他类似的方法一样，SCA倾向于陷入次优区域，这反映在寻找最佳值所需的计算工作量上。出现这种情况的原因是用于探测的运算符不能很好地分析搜索空间。本文提出了一种改进的SCA算法，它将基于对立的学习(OBL)作为一种机制，以更好地探索搜索空间，产生更准确的解。OBL是一种机器学习策略，通常用于提高元启发式算法的性能。OBL考虑解在搜索空间中的相对位置。该算法根据目标函数值，在原始解与其对应位置之间选择最佳元素，从而提高了优化过程的精度。来自不同领域的概念的混合在智能和专家系统中是至关重要的；它有助于结合算法的优势以生成更有效的方法。提出的方法是这种结合的一个例子；它已经在几个基准函数和工程问题上进行了测试。这些结果支持了所提出的方法在复杂搜索空间中寻找最优解的有效性。(C)2017爱思唯尔有限公司。保留所有权利。
Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces. (C) 2017 Elsevier Ltd. All rights reserved.