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A Novel Meta-Heuristic Optimization Algorithm: Current Search

机译:一种新颖的元启发式优化算法:当前搜索

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摘要

Inspired by an electric current flowing through electric networks, a novel meta-heuristic optimization algorithm named the Current Search (CS) is proposed in this article. The proposed CS algorithm is an optimization algorithm based on the intelligent behavior of electric current flowing through open and short circuits. To perform its effectiveness and robustness, the proposed CS algorithm is tested against five well-known benchmark continuous multivariable test functions collected by Ali et al. The results obtained by the proposed CS are compared with those obtained by the popular search techniques widely used to solve optimization problems, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Tabu Search (TS). The results show that the proposed CS outperforms other algorithms. The results obtained by the proposed CS are superior within reasonable time consumed.
机译:受到流经电网的电流的启发,本文提出了一种新颖的元启发式优化算法,称为电流搜索(CS)。提出的CS算法是一种基于流经开路和短路电流的智能行为的优化算法。为了执行其有效性和鲁棒性,针对Ali等人收集的五个众所周知的基准连续多变量测试函数对提出的CS算法进行了测试。将通过提议的CS获得的结果与通过广泛用于解决优化问题的流行搜索技术(即遗传算法(GA),粒子群优化(PSO)和禁忌搜索(TS))获得的结果进行比较。结果表明,所提出的CS优于其他算法。提议的CS获得的结果在合理的时间内消耗效果更好。

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