artificialbeecolony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve...
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artificialbeecolony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.
The minimum attribute reduction problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial optimization problem. In this paper, we propose an efficient and competitive combinatorial...
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The minimum attribute reduction problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial optimization problem. In this paper, we propose an efficient and competitive combinatorial artificial bee colony algorithm for solving the minimum attribute reduction problem. In the proposed algorithm, a new multidimensional binary local search scheme for bee colonies based on velocity computation is presented;an employed bee and its recruited onlooker bees use different local search strategies so as to get a possibly more diversified neighboring search around a current food source;the information of the so-far best solution is exploited in various ways by employed bees, onlookers and scouts, respectively;the monotonicity property of classification quality of attribute subsets from the theory of rough sets is employed to avoid possibly invalid local searches. Performance comparisons with some best performing population-based metaheuristic algorithms for the minimum attribute reduction problem were carried out on a number of UCI data sets. The experimental results show that the proposed algorithm overall outperforms all the other algorithms in terms of solution quality and is therefore promising for solving the minimum attribute reduction problem.
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