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Computer Science > Neural and Evolutionary Computing

arXiv:2301.02795 (cs)
[Submitted on 7 Jan 2023]

Title:A Lite Fireworks Algorithm for Optimization

Authors:Haimiao Mo, Min Zeng
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Abstract:The fireworks algorithm is an optimization algorithm for simulating the explosion phenomenon of fireworks. Because of its fast convergence and high precision, it is widely used in pattern recognition, optimal scheduling, and other fields. However, most of the existing research work on the fireworks algorithm is improved based on its defects, and little consideration is given to reducing the number of parameters of the fireworks algorithm. The original fireworks algorithm has too many parameters, which increases the cost of algorithm adjustment and is not conducive to engineering applications. In addition, in the fireworks population, the unselected individuals are discarded, thus causing a waste of their location information. To reduce the number of parameters of the original Fireworks Algorithm and make full use of the location information of discarded individuals, we propose a simplified version of the Fireworks Algorithm. It reduces the number of algorithm parameters by redesigning the explosion operator of the fireworks algorithm and constructs an adaptive explosion radius by using the historical optimal information to balance the local mining and global exploration capabilities. The comparative experimental results of function optimization show that the overall performance of our proposed LFWA is better than that of comparative algorithms, such as the fireworks algorithm, particle swarm algorithm, and bat algorithm.
Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
Cite as: arXiv:2301.02795 [cs.NE]
  (or arXiv:2301.02795v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2301.02795

Submission history

From: Haimiao Mo [view email]
[v1] Sat, 7 Jan 2023 07:12:17 UTC (6,660 KB)
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