This research proposes a modified metaheuristic optimization algorithm, named as improved stochastic fractal search (ISFS), which is formed based on the integration of the quasiopposition-based learning (QOBL) and chaotic local search (CLS) schemes into the original SFS algorithm for solving the optimal capacitor placement (OCP) in radial distribution networks (RDNs). The test problem involves the determination of the optimal number, location, and size of fixed and switched capacitors at different loading conditions so that the network total yearly cost is minimized with simultaneous fulfillment of operating constraints. Also, the hourly on/off scheduling plans of switched shunt capacitors (SCs) considering a modified cost objective function are obtained. The proposed ISFS algorithm has been tested on two IEEE 69-bus and 119-bus RDNs and a practical 152-bus RDN. For clarifying the effectiveness and validation of the ISFS, the simulated results have been compared with those of other previously utilized solution approaches in the literature as well as the original SFS. From result comparison, the proposed ISFS outperforms other previous approaches regarding solution quality and statistical performance for the compared cases, especially in the complex and large-scale networks. Notably, compared with the original SFS, the proposed ISFS shows a significantly better performance in all the tested cases.

中文翻译：

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改进的随机分形搜索算法在径向配电网电容器分配问题的成本效益分析

本研究提出了一种改进的元启发式优化算法，称为改进的随机分形搜索（ISFS），它是基于准对位学习（QOBL）和混沌局部搜索（CLS）方案与原始SFS算法的集成而形成的径向分布网络（RDN）中的最佳电容器放置（OCP）。测试问题涉及确定在不同负载条件下固定和开关电容器的最佳数量，位置和尺寸，以便在同时满足操作约束的情况下将网络的年度总成本降至最低。而且，获得考虑了修改后的成本目标函数的开关式并联电容器（SC）的每小时开/关调度计划。提议的ISFS算法已经在两个IEEE 69总线和119总线RDN和一个实际的152总线RDN上进行了测试。为了阐明ISFS的有效性和有效性，已将模拟结果与文献中其他先前使用的解决方案方法以及原始SFS的结果进行了比较。从结果比较来看，在比较情况下，尤其是在复杂的大型网络中，建议的ISFS在解决方案质量和统计性能方面优于其他方法。值得注意的是，与原始SFS相比，建议的ISFS在所有测试情况下均表现出明显更好的性能。仿真结果已与文献中其他先前使用的解决方案方法以及原始SFS进行了比较。从结果比较来看，在比较情况下，尤其是在复杂的大型网络中，建议的ISFS在解决方案质量和统计性能方面优于其他方法。值得注意的是，与原始SFS相比，建议的ISFS在所有测试情况下均表现出明显更好的性能。仿真结果已与文献中其他先前使用的解决方案方法以及原始SFS进行了比较。从结果比较来看，在比较情况下，尤其是在复杂的大型网络中，建议的ISFS在解决方案质量和统计性能方面优于其他方法。值得注意的是，与原始SFS相比，建议的ISFS在所有测试情况下均表现出明显更好的性能。