Evaluation and comparison of the performance of the horse herd algorithm with some meta-heuristic algorithms
Subject Areas : Machine learning (deep learning, artificial neural networks, evolutionary algorithms, data mining, etc.)Jalal Iziy 1 , Ali Akbar Neghabi 2
1 - PhD student, Department of Computer Engineering and Information Technology, Islamic Azad University, Sabzevar Branch, Sabzevar, Iran
2 - Computer Engineering and information technology Department, Islamic Azad University, Sabzevar Branch
Keywords: meta-heuristic algorithms, optimization, horse herd algorithm, whale algorithm, forty thieves algorithm and Alibaba.,
Abstract :
Meta-heuristic algorithms have become a popular tool for solving many problems in real-world applications due to their ability to overcome many problems in traditional optimization. The performance of these algorithms is different in different problems, so it is necessary to make a careful evaluation of their performance. One of the meta-heuristic algorithms that has recently attracted a lot of attention is the horse herd algorithm, which is inspired by the behavior of horses at different ages. The purpose of this research is to compare and evaluate the performance of the horse herd algorithm with some other meta-heuristic algorithms for solving complex problems. In this study, the performance of horse herd algorithm is compared with 9 other meta-heuristic algorithms including ant colony, forty thieves and Alibaba, ant lion, bat, crow search, firefly, genetics, particle swarm and whale algorithm. In this evaluation, 10 standard test functions were used and the performance of the algorithms was compared based on three criteria: best answer, standard deviation, and execution time in dimensions of 500, 1000, and 2000. The simulation results show that due to the large number of parameters that the horse herd algorithm has, one of the challenges of this algorithm is to adjust its parameters. Also, in high dimensions, the horse herd algorithm does not perform well compared to other compared meta-heuristic algorithms.
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