期刊
APPLIED SOFT COMPUTING
卷 86, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2019.105884
关键词
Bacterial Foraging Optimization; Chaotic local search; Gaussian mutation; Kernel extreme learning machine
资金
- National Natural Science Foundation of China [61922064]
- Zhejiang Provincial Natural Science Foundation, China [LR17F030001]
- Science and Technology Plan Project of Wenzhou, China [ZG2017016, ZG2017019]
- Graduate Scientific Research Foundation of Wenzhou University, China [3162018022, 3162018024]
- Zhejiang University Students Science and Technology Innovation Activity Plan, China [2019R429043]
The Bacterial Foraging Optimization (BFO) algorithm is a swarm intelligent algorithm widely used in various optimization problems. However, BFO suffers from multiple drawbacks, including slow convergence speed, inability to jump out of local optima and fixed step length. In this study, an enhanced BFO with chaotic chemotaxis step length, Gaussian mutation and chaotic local search (CCGBFO) is proposed for overcoming the existing weakness of original BFO. First, a chaotic chemotaxis step length operation is used to produce adaptive chemotaxis step length. Then, by combining the optimal position in the current bacteria with the Gaussian mutation operation to make full use of the information of the optimal position. Finally, a chaotic local search is introduced into the chemotaxis step to ensure that the algorithm can explore a large search space in the early stage. The performance of CCGBFO was evaluated on a comprehensive set of numerical benchmark functions including IEEE CEC2014 and CEC2017 problems. In addition, CCGBFO was also used to tune the key parameters of kernel extreme learning machine for dealing with the real-world problems. The experimental results show that the proposed CCGBFO significantly outperforms the original BFO in terms of both convergence speed and solution accuracy. (C) 2019 Elsevier B.V. All rights reserved.
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