Improved antlion optimization algorithm via tournament selection

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI:10.1109/CICN.2017.8319385

Haydar Kiliç, U. Yuzgec

引用次数: 4

Abstract

From the measurement point of view, it is observed that antlion optimization algorithm (ALO) runs slower than other heuristic algorithms and it needs to be improved in terms of optimality and accuracy. For this reason, improved antlion optimization algorithm via tournament selection (IALOT) is presented in this study. IALOT, ALO, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms have been evaluated using benchmark test functions such as time, optimality, accuracy, CPU time, number of function evaluations (NFE), mean best solution and standard deviation. In summary, elite antlion selection, random walks, and other parts of the antlion optimization algorithm have been developed. As a result, the IALOT algorithm has shown better results than ALO algorithm.

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通过比赛选择改进了antlion优化算法

从测量的角度来看，antlion优化算法(ALO)的运行速度比其他启发式算法要慢，在最优性和精度方面有待改进。为此，本文提出了一种基于锦标赛选择的改进蚁群优化算法(IALOT)。采用基准测试函数，如时间、最优性、精度、CPU时间、函数评估次数(NFE)、平均最优解和标准差，对IALOT、ALO、粒子群优化(PSO)和人工蜂群(ABC)算法进行了评估。综上所述，精英蚁群选择、随机漫步和蚁群优化算法的其他部分已经开发出来。结果表明，IALOT算法比ALO算法表现出更好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)

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