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Appl. Intell. 01/2011; 35:436-444.
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Annals OR. 01/2011; 186:383-391.
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Pattern Recognition. 01/2011; 44:2502-2515.
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
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Annals OR. 01/2010; 181:303-324.
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J. Heuristics. 01/2010; 16:65-83.
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Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, Pisa, Italy , November 30-December 2, 2009; 01/2009
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ABSTRACT: In this paper, a novel ant colony optimization algorithm is proposed for the orienteering problem. This algorithm can adaptively choose the lower trail limit to avoid stagnation. To study its performance, we compare the proposed algorithm to max-min ant system with and without re-initialization. The experimental results demonstrate that the performance of our algorithm is competitive.
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on; 07/2008
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ABSTRACT: In this paper, we present an ant colony optimization (ACO) approach to solve the set covering problem. A constraint-oriented solution construction method is proposed. The main difference between it and the existing method is that, while adding a column to the current partial solution, it randomly selects an uncovered row and only considers the columns covering the row, but not all the unselected columns as candidate solution components. This decreases the number of candidate solution components and therefore accelerates the run speed of the algorithm. Moreover, a simple but effective local search procedure, which aims at eliminating redundant columns and replacing some columns with more effective ones, is developed to improve the quality of solutions constructed by ants while keeping their feasibility. The proposed algorithm has been tested on a number of benchmark instances. Computational results indicate that it is capable of producing high quality solutions and performs better than the existing ACO-based algorithms.
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on; 07/2008
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ABSTRACT: Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we introduce a new approach based on ant colony optimization (ACO) for attribute reduction. To verify the proposed algorithm, numerical experiments are carried out on thirteen small or medium-sized datasets and three gene expression datasets. The results demonstrate that this algorithm can provide competitive solutions efficiently.
Pattern Recognition Letters.
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ABSTRACT: The team orienteering problem (TOP) involves finding a set of paths from the starting point to the ending point such that the total collected reward received from visiting a subset of locations is maximized and the length of each path is restricted by a pre-specified limit. In this paper, an ant colony optimization (ACO) approach is proposed for the team orienteering problem. Four methods, i.e., the sequential, deterministic-concurrent and random-concurrent and simultaneous methods, are proposed to construct candidate solutions in the framework of ACO. We compare these methods according to the results obtained on well-known problems from the literature. Finally, we compare the algorithm with several existing algorithms. The results show that our algorithm is promising.
Computers & Industrial Engineering.
