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A binary social spider algorithm for uncapacitated facility location problem
Expert Systems with Applications ( IF 7.5 ) Pub Date : 2020-06-29 , DOI: 10.1016/j.eswa.2020.113618
Emine Baş , Erkan Ülker

In order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of the Social Spider Algorithm called Binary Social Spider Algorithm (BinSSA) is proposed for binary optimization problems. BinSSA is obtained from SSA, by transforming constant search space to binary search space with four transfer functions. Thus, BinSSA variations are created as BinSSA1, BinSSA2, BinSSA3, and BinSSA4. The study steps of the original SSA are re-updated for BinSSA. A random walking schema in SSA is replaced by a candidate solution schema in BinSSA. Two new methods (similarity measure and logic gate) are used in candidate solution production schema for increasing the exploration and exploitation capacity of BinSSA. The performance of both techniques on BinSSA is examined. BinSSA is named as BinSSA(Sim&Logic). Local search and global search performance of BinSSA is increased by these two methods. Three different studies are performed with BinSSA. In the first study, the performance of BinSSA is tested on the classic eighteen unimodal and multimodal benchmark functions. Thus, the best variation of BinSSA and BinSSA(Sim&Logic) is determined as BinSSA4(Sim&Logic). BinSSA4(Sim&Logic) has been compared with other heuristic algorithms on CEC2005 and CEC2015 functions. In the second study, the uncapacitated facility location problems (UFLPs) are solved with BinSSA(Sim&Logic). UFL problems are one of the pure binary optimization problems. BinSSA is tested on low-scaled, middle-scaled, and large-scaled fifteen UFLP samples and obtained results are compared with eighteen state-of-art algorithms. In the third study, we solved UFL problems on a different dataset named M* with BinSSA(Sim&Logic). The results of BinSSA(Sim&Logic) are compared with the Local Search (LS), Tabu Search (TS), and Improved Scatter Search (ISS) algorithms. Obtained results have shown that BinSSA offers quality and stable solutions.



中文翻译:

一种无能力设施定位问题的二进制社交蜘蛛算法

为了找到解决现实世界中复杂问题的有效解决方案,通常使用计算机科学,尤其是启发式算法。启发式算法可以在可接受的时间内为大规模优化问题提供最佳解决方案。研究了基于蜘蛛行为的启发式算法社会蜘蛛算法(SSA)。提出了对该算法的原始研究以解决连续问题。本文针对二进制优化问题,提出了社会蜘蛛算法的二进制版本,称为二进制社会蜘蛛算法(BinSSA)。通过使用四个传递函数将恒定搜索空间转换为二进制搜索空间,可以从SSA获得BinSSA。因此,将BinSSA变体创建为BinSSA1,BinSSA2,BinSSA3和BinSSA4。原始SSA的研究步骤已针对BinSSA重新更新。SSA中的随机行走方案被BinSSA中的候选解决方案方案替代。候选解决方案生产方案中使用了两种新方法(相似性度量和逻辑门)来提高BinSSA的探索和开发能力。检查了两种技术在BinSSA上的性能。BinSSA被命名为BinSSA(Sim&Logic)。通过这两种方法,可以提高BinSSA的本地搜索和全局搜索性能。BinSSA进行了三个不同的研究。在第一个研究中,在经典的18个单峰和多峰基准函数上测试BinSSA的性能。因此,将BinSSA和BinSSA(Sim&Logic)的最佳变化确定为BinSSA4(Sim&Logic)。BinSSA4(Sim&Logic)已与CEC2005和CEC2015功能上的其他启发式算法进行了比较。在第二项研究中 通过BinSSA(Sim&Logic)解决了无能力的设施位置问题(UFLP)。UFL问题是纯二进制优化问题之一。BinSSA在低级,中级和大型15个UFLP样本上进行了测试,并将获得的结果与18个最新算法进行了比较。在第三项研究中,我们使用BinSSA(Sim&Logic)在名为M *的另一个数据集上解决了UFL问题。BinSSA(Sim&Logic)的结果与本地搜索(LS),禁忌搜索(TS)和改进的分散搜索(ISS)算法进行了比较。取得的结果表明BinSSA提供了质量稳定的解决方案。和大规模的15个UFLP样本,并将获得的结果与18个最新算法进行比较。在第三项研究中,我们使用BinSSA(Sim&Logic)在名为M *的另一个数据集上解决了UFL问题。BinSSA(Sim&Logic)的结果与本地搜索(LS),禁忌搜索(TS)和改进的分散搜索(ISS)算法进行了比较。取得的结果表明BinSSA提供了质量稳定的解决方案。和大规模的15个UFLP样本,并将获得的结果与18个最新算法进行比较。在第三项研究中,我们使用BinSSA(Sim&Logic)在名为M *的另一个数据集上解决了UFL问题。BinSSA(Sim&Logic)的结果与本地搜索(LS),禁忌搜索(TS)和改进的分散搜索(ISS)算法进行了比较。取得的结果表明BinSSA提供了质量稳定的解决方案。

更新日期:2020-06-29
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