An accurate partially attracted firefly algorithm
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The firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed by adopting a partial attraction model and a fast attractiveness calculation strategy. The partial attraction model can preserve swarm diversity and make full use of individual information. The fast attractiveness calculation strategy ensures information sharing among the individuals and it also improves the convergence accuracy. The experimental results demonstrate the good performance of PaFA in terms of the solution accuracy compared with two state-of-the-art FA variants and two other bio-inspired algorithms.
KeywordsFirefly algorithm Partial attraction model Fast attractiveness calculation Optimization
Mathematics Subject Classification68T20
The authors thank the Chinese National Natural Science Foundation (No. 61379059) and the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities (No. CZY18012) for financial support for this work.
Compliance with ethical standards
Conflict of interest
The authors declare that there are no conflict of interests regarding the publication of this paper.
Informed consent was obtained from all authors included in the study. This manuscript does not contain any studies with human participants or animals performed by any of the authors.
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