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• W4313381344 abstract "Abstract The traveling salesman problem (TSP) is an NP-hard problem. Thus far, a large number of researchers have proposed different ant colony optimization (ACO) algorithms to solve the TSP. These algorithms inevitably encounter problems such as long convergence time and the tendency to easily fall into local optima. On the basis of the ACO algorithm, this study proposes a dynamic adaptive ACO algorithm (DAACO). DAACO realizes the diversity of initialization of the ACO algorithm by dynamically determining the number of ants to be prevented from falling into local optimization. DAACO also adopts a hybrid local selection strategy to increase the quality of ant optimization and reduce the optimization time. Among the 20 instances of the TSPLIB dataset, the DAACO algorithm obtains 19 optimal values, and the solutions of 10 instances are better than those of other algorithms. The experimental results on the TSPLIB dataset show that the DAACO algorithm has obvious advantages in terms of convergence time, solution quality, and average value relative to existing state-of-the-art ACO algorithms." @default.
• W4313381344 created "2023-01-06" @default.
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• W4313381344 date "2023-01-02" @default.
• W4313381344 modified "2023-10-12" @default.
• W4313381344 title "DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem" @default.
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• W4313381344 doi "https://doi.org/10.1007/s40747-022-00949-6" @default.
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