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Comparative analysis of hybrid optimization algorithms combined artificial rabbits optimization and arctic puffin optimization
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Abstract
The paper delivers a breakthrough in the optimization field by combining two nature-inspired algorithms: Arctic Puffin Optimization (APO) and Artificial Rabbits Optimization (ARO). The research proposes five hybrid strategies, such as sequential, alternating, and parallel, to take advantage of the global exploration capability of APO and exploit the local efficiency of ARO. The hybrid strategies are evaluated by using six standard Benchmark funtions with different problem dimensions. The results show that the hybrid strategies, especially the alternating and parallel ones, outperform ARO and APO independently in terms of convergence speed, accuracy, and local extreme avoidance. This combination deploys the global exploration capabilities of APO and the local exploitation capabilities of ARO, providing effective solutions to multidimensional optimization problems. Future development direction focuses on automatic tuning of hybrid strategies and practical applications, such as route optimization or energy management.
Keywords
APO, ARO, Hybrid Algorithm, Optimization
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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