NDLI: GSP-ANT: An efficient ant colony optimization algorithm with multiple good solutions for pheromone update

Content Provider	IEEE Xplore Digital Library
Author	Zhigang Ren Zuren Feng Zhaojun Zhang
Copyright Year	2009
Description	Author affiliation: Systems Engineering Institute, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, China (Zhigang Ren; Zuren Feng; Zhaojun Zhang)
Abstract	Ant colony optimization (ACO) is a metaheuristic for various optimization problems, especially the hard combinatorial optimization problems. However, existing ACO algorithms suffer from search stagnation and exorbitantly long computation time. To alleviate these shortcomings, an improved ACO algorithm, called GSP-ANT, is presented in this paper. It maintains a good solution pool (GSP) and alternately uses the optimal solution and suboptimal solutions in the pool to update pheromone. This enables ants to transfer among different solution regions and accordingly explore larger solution space. On the other hand, once a solution in the GSP is selected, it is continuously used for pheromone update in a certain number of iterations with the aim of exploiting the neighborhood of this solution intensively. By this means, both the intensification and diversification of the search are considered. The performance of GSP-ANT is examined experimentally on typical traveling salesman problems. Computational results indicate that GSP-ANT is a promising approach.
Starting Page	589
Ending Page	592
File Size	97703
Page Count	4
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5357772
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Laboratories Traveling salesman problems Genetic algorithms Search stagnation Pheromone update Simulated annealing Good solution pool Systems engineering and theory Space exploration Large-scale systems Manufacturing systems Testing
Content Type	Text
Resource Type	Article

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