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Improved Bee Colony Optimization for Traveling Salesman Problem
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An improved artificial bee colony algorithm is proposed for traveling salesman problem, which is a classical NP- hard problem. By improved artificial bee colony algorithm we introduce swarm behavior of the artificial fish swarm algorithm and the crossover operator of the genetic algorithm, and enlightens the underdamping motion of physics which uses it to adaptively update for the visual range of the artificial fish swarm algorithm. Through the simulation of six classic traveling salesman problems in the TSPLIB standard library and comparison with other improved algorithms, we show that the improved artificial bee colony algorithm has a better performance and better results than the original artificial bee colony algorithm in solving the TSP problem.
Title: Improved Bee Colony Optimization for Traveling Salesman Problem
Description:
An improved artificial bee colony algorithm is proposed for traveling salesman problem, which is a classical NP- hard problem.
By improved artificial bee colony algorithm we introduce swarm behavior of the artificial fish swarm algorithm and the crossover operator of the genetic algorithm, and enlightens the underdamping motion of physics which uses it to adaptively update for the visual range of the artificial fish swarm algorithm.
Through the simulation of six classic traveling salesman problems in the TSPLIB standard library and comparison with other improved algorithms, we show that the improved artificial bee colony algorithm has a better performance and better results than the original artificial bee colony algorithm in solving the TSP problem.
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