Binary-State Bacterial Foraging Optimization Based on Network Topology and its Application. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Wang, Sun'an; Wu, Shenli; Li, Xiaohu; Kang, Chenlong
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 2(2017)
• Page Start: 271
• Page End: 284
• Publication Date: 2017-04-03
• Subjects: Bacterial foraging optimization -- binary-state -- von Neumann topology -- small-world networks -- controller design; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2016.1205823
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: Bacterial foraging optimization (BFO) inspired by the foraging behavior of E.coli has been used to solve optimization problems. This paper presents a novel binary-state bacterial foraging optimization based on network topology (BBFO-NT). In the proposed BBFO-NT, a binary-state bacterial foraging strategy, which makes the bacteria to have mutual learning mechanism, is introduced. The two behavioral states include an explorative state based on Von Neumann topology and an exploitative state based on small-world networks. The bacteria co-evolve during the optimization process under the two states. Experiments on a set of benchmark functions validate the effectiveness of the improved algorithm. BFO and some other intelligent optimization algorithms are employed for comparison. The simulations show that the proposed BBFO-NT offers significant improvements than BFO. On this basis, the new algorithm has been successfully applied to the docking control. The experiments indicate that the improved algorithm is effective in controller design.