A Modified Artificial Rabbits Optimization for Solving Numerical Functions and Engineering Problems

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書誌詳細
出版年:	International Journal of Swarm Intelligence Research vol. 16, no. 1 (2025), p. 1-39
第一著者:	Yuan, Qihang
その他の著者:	Zhang, Yongde, Muzzammil, Hafiz Muhammad
出版事項:	IGI Global
主題:	Accuracy Swarm intelligence Engineering Physics Convergence Evolution Artificial intelligence Exploitation Optimization algorithms Optimization techniques Optimization
オンライン･アクセス:	Citation/Abstract Full Text - PDF
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その他の書誌記述
抄録:	The recently proposed swarm intelligence Artificial Rabbits Optimization (ARO) performs well, but there are still some drawbacks, including low population diversity, unbalanced exploration and exploitation capabilities, and low convergence accuracy. To address the above issues, this article proposes a variant of ARO named MARO, which adopts three strategies to overcome the limitations of ARO and improve its performance. This paper uses 23 classic test functions and CEC2017 test functions for testing. The experimental results show that MARO has higher convergence speed, accuracy, and stability than the comparison algorithms. In addition, the enormous potential of MARO in practical applications is further verified through five real-world engineering application problems.
ISSN:	1947-9263 1947-9271
DOI:	10.4018/IJSIR.378562
ソース:	Engineering Database