[1]MA Shenglan,YE Dongyi.A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability[J].CAAI Transactions on Intelligent Systems,2011,6(2):132-140.
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A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 2 Page number: 132-140 Column: 学术论文—人工智能基础 Public date: 2011-04-25
Title:
A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability
Author(s):
MA Shenglan;  YE Dongyi
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108， China
Keywords:
attribute reduction;  rough set;  tabu search;  particle swarm optimization;  parallel particle subswarm
CLC:
TP18
DOI:
-
Abstract:
In order to improve the solution quality and computing efficiency of rough set minimum attribute reduction algorithms based on swarm intelligence, a parallel particle subswarm optimization algorithm with longmemory Tabu search capability was proposed. In addition to the taboo restriction, some diversification and intensification schemes were employed. Since parallel subswarms have a lower probability of simultaneously getting trapped in a local optimum than a single particle swarm, the proposed algorithm enhances the probability of finding a global optimum and decreases the influence of initial particles. Experimental results on a number of UCI datasets show that the proposed algorithm has a higher probability of finding a minimum attribute reduction in rough sets compared with some existing swarm intelligence based attribute reduction algorithms. Therefore, the proposed algorithm is feasible and relatively effective for the minimum attribute reduction problem.
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