Application an improved swarming optimisation in attribute reduction
by Yi Zhang; Xingjuan Cai; Honglie Zhu; Yong Xu
International Journal of Bio-Inspired Computation (IJBIC), Vol. 16, No. 4, 2020

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.

Online publication date: Tue, 12-Jan-2021

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