Ant colony optimization-differential evolution fusion method for solving traveling salesman problems
A technique of traveling salesman problem and differential evolution, applied in the fusion field of ant colony optimization-differential evolution
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[0088] In order to verify the superiority of the improved ant colony optimization-differential evolution optimization algorithm when solving the traveling salesman problem, the present invention utilizes 48 cities traveling salesman problem (Att48TSP) to carry out experiments, and its specific implementation steps are as follows:
[0089] Step 1: Parameter initialization: set the current number of iterations Nc=1, and set the maximum number of loop iterations Nc max =100, parameter assignment: m=30, α=2, β=4, ρ=0.7, Q=10, F=2, CR=0.5, Team=5.
[0090] Step 2: Input the city coordinate set C in the 48-city traveling salesman problem to be solved, the total number of cities is n=48, and calculate the path length d between city j and city k jk , and η jk =1 / d jk Let the initialization pheromone τ on the path connecting city j and city k j,k =1, assign all ants to each ant team, the number of ants in each team is T_m(i)=m / Team=6; place the ants of each team randomly on each cit...
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