Translated Title
An Efficient Combinatorial Artificial Bee Colony Algorithm for Solving Minimum Attribute Reduction Problem
Translated Abstract
Minimum attribute reduction (MAR) problem in the context of rough set theory is an NP-hard nonlinearly con-strained combinatorial (binary ) optimization problem .In this paper ,a new combinatorial artificial bee colony (ABC ) algorithm is presented for solving the MAR problem .Mutation operation based search schemes are introduced for employed bees ,onlooker bees and scout bees .Two different metrics related to attribute subsets are used to generate candidate neighboring food sources .Different local search strategies between an employed bee and its recruited onlooker bees allow for a more diversified neighboring search around a current food source .Moreover ,the information of the so-far best solution is exploited in various ways by employed bees , onlookers and scouts ,respectively .Performance comparisons with existing best performing meta-heuristic approaches for the MAR problem were carried out on a number of UCI data sets .In addition ,a standard statistical t-test is used for evaluation purpose .The experimental results show that our combinatorial ABC approach compares favorably with all the other approaches in terms of solu-tion quality .The proposed combinatorial ABC algorithm is thus efficient and well suited for solving the MAR problem .
Translated Keyword
combinatorial artificial bee colony algorithrn
local search schemes
meta-heuristic approach
minimum attribute reduction
rough sets
Access Number
WF:perioarticaldianzixb201505027
