Abstract
Harris Hawk Optimizer (HHO) is a new algorithm based on population, because of the diversity of its plunder strategy, it has good exploration ability, but there is still room for further improvement of exploitation ability. Because of its unique “explosion” mechanism, Fireworks Algorithm (FWA) has good exploitation ability. In order to make up for the shortcomings of HHO algorithm, this paper proposes an improved HHO algorithm, fireworks Harris hawk algorithm based on dynamic competition mechanism (DCFW-HHO). In the iterative process, taking the escape energy function of HHO algorithm as an index, different competition mechanisms and fireworks explosion operations are performed in different stages of the algorithm. In order to verify the performance of the proposed algorithm, the benchmark function of CEC2005 is optimized by DCFW-HHO, and compared with the marine predator algorithm (MPA), whale optimization algorithm (WOA), lightning search algorithm (LSA), water cycle algorithm (WCA), FWA and HHO, experiments show that the proposed DCFW-HHO algorithm has strong optimization ability.
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Acknowledgements
This research was funded in part by the National Natural Science Foundation of China under grant number 61801521 and 61971450, in part by the Natural Science Foundation of Hunan Province under grant number 2018JJ2533, and in part by the Fundamental Research Funds for the Central Universities under grant number 2018gczd014 and 20190038020050.
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Li, W., Shi, R., Zou, H., Dong, J. (2021). Fireworks Harris Hawk Algorithm Based on Dynamic Competition Mechanism for Numerical Optimization. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_40
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