An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine

• Published in: Applied soft computing Vol. 86; p. 105884
• Main Authors: Chen, Huiling, Zhang, Qian, Luo, Jie, Xu, Yueting, Zhang, Xiaoqin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2020
• Subjects: Bacterial Foraging Optimization; Chaotic local search; Gaussian mutation; Kernel extreme learning machine; Chaotic local search; Bacterial Foraging Optimization; Gaussian mutation; Kernel extreme learning machine
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105884

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. •This paper proposes an enhanced Bacterial Foraging Optimization (BFO) algorithm for global optimization.•Chaotic chemotaxis step length, Gaussian mutation and chaotic local search are integrated with BFO algorithm.•The effectiveness was proved by the extensive results on benchmark problems and real-world problems.