An improved ant colony optimization (I-ACO) method for the quasi travelling salesman problem (Quasi-TSP) |
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| Authors: | Jianyi Yang Ruifeng Ding Yuan Zhang Maoqin Cong Fei Wang |
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| Institution: | 1. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, Jiangsu 210023, China;2. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, Jiangsu 210023, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China;4. Sichuan Quality Supervision and Testing Center of Surveying and Mapping Product, Chengdu, Sichuan 610041, China |
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| Abstract: | Traveling salesman problem (TSP) and its quasi problem (Quasi-TSP) are typical problems in path optimization, and ant colony optimization (ACO) algorithm is considered as an effective way to solve TSP. However, when the problems come to high dimensions, the classic algorithm works with low efficiency and accuracy, and usually cannot obtain an ideal solution. To overcome the shortcoming of the classic algorithm, this paper proposes an improved ant colony optimization (I-ACO) algorithm which combines swarm intelligence with local search to improve the efficiency and accuracy of the algorithm. Experiments are carried out to verify the availability and analyze the performance of I-ACO algorithm, which cites a Quasi-TSP based on a practical problem in a tourist area. The results illustrate the higher accuracy and efficiency of the I-ACO algorithm to solve Quasi-TSP, comparing with greedy algorithm, simulated annealing, classic ant colony algorithm and particle swarm optimization algorithm, and prove that the I-ACO algorithm is a positive effective way to tackle Quasi-TSP. |
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| Keywords: | path optimization Quasi-TSP swarm intelligence I-ACO |
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