[1]WEI Weiyi,WEN Yahong.Firefly optimization algorithm utilizing elite opposition-based learning[J].CAAI Transactions on Intelligent Systems,2017,12(5):710-716.[doi:10.11992/tis.201706014]
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Firefly optimization algorithm utilizing elite opposition-based learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 710-716 Column: 学术论文—机器学习 Public date: 2017-10-25
Title:
Firefly optimization algorithm utilizing elite opposition-based learning
Author(s):
WEI Weiyi;  WEN Yahong
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Keywords:
firefly algorithm; elite opposition-based learning; optimized algorithm; elite group; opposite solutions; opposition-based learning strategy; differential evolutionary mutation; adaptive step size
CLC:
TP309.2
DOI:
10.11992/tis.201706014
Abstract:
To increase the convergence speed and solution accuracy of the traditional firefly algorithm, in this paper, we propose a firefly optimization algorithm that utilizes elite opposition-based learning. Using an opposition-based learning strategy, we constructed an elite group and, in the interval of the elite group, we solved the opposite solutions of the ordinary groups. This strategy could increase group diversity and improve the convergence speed of the algorithm. To prevent the optimal individual from falling into the local optimum, which could cause stagnation of the whole group during the search process, we introduce a differential evolutionary mutation strategy. Finally, we propose an adaptive step size with a linear decrease to balance the development ability of the algorithm. Experimental results show that the proposed algorithm can increase convergence speed and accuracy.
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