[1]TANG Shaohu,LIU Xiaoming.An improved adaptive step glowworm swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(3):470-475.[doi:10.3969/j.issn.1673-4785.201403025]
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An improved adaptive step glowworm swarm optimization algorithm
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
10
Number of periods:
2015 3
Page number:
470-475
Column:
学术论文—人工智能基础
Public date:
2015-06-25
- Title:
- An improved adaptive step glowworm swarm optimization algorithm
- CLC:
- TP183
- DOI:
- 10.3969/j.issn.1673-4785.201403025
- Abstract:
- In the basic glowworm swarm optimization (GSO), it is easy to fall into local optimum and the oscillation phenomenon of function adaptive values may occur because of the fixed step length. In some adaptive-step glowworm swarm optimization (A-GSO) algorithms, neighborhood sets of some fireflies may be empty in the iterative process of the algorithm, which leads to lower convergence speed and falls into local optimal value. Therefore, an improved foraging-behavior adaptive-step GSO (FA-GSO) algorithm was designed. The foraging behavior of the fireflies without neighborhood peer and adaptive step is introduced in order to find the optimization direction in the improved algorithm. The precision, stability, and global convergence analysis of FA-GSO is presented. After extracting and comparing the relevant optimization indicators of GSO, A-GSO and FA-GSO by several standard test functions, the effectiveness of the FA-GSO algorithm was verified, which indicates that the improved algorithm can improve the accuracy of function optimization and the iteration speed.
- References:
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Last Update:
2015-07-15