Title: Application an improved swarming optimisation in attribute reduction
Authors: Yi Zhang; Xingjuan Cai; Honglie Zhu; Yong Xu
Addresses: College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China ' College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan China ' College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China ' College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China
Abstract: In order to solve the attribute reduction algorithm's drawbacks of the extensive range of initial searching space and low convergence rate in the latter stage, we present an improved swarming optimisation base on ant colony optimisation in this paper. By strengthening the pheromone concentration of critical pipelines, the probability of the vital pipeline being selected in the path optimisation process is increased, thereby improving the development of the optimal solution by the ant colony algorithm. The improved algorithm puts forward attributes' of and adaptive choice model. The adaptive choice model enhances the possibility of choosing high-quality results. Simulation results show that the improved algorithm's success rate converges to the optimal results. Moreover, the improved algorithm has the capability of high accuracy and fast convergence.
Keywords: swarming algorithm; attribute reduction; ant colony optimisation.
DOI: 10.1504/IJBIC.2020.112353
International Journal of Bio-Inspired Computation, 2020 Vol.16 No.4, pp.213 - 219
Received: 04 Aug 2020
Accepted: 03 Sep 2020
Published online: 12 Jan 2021 *