A Novel Collaborative Optimization Algorithm for Solving TSP

Abstract:

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 1795-1798
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.1795
• Online since: March 2014
• Authors: Di Zhou*
• Keywords: Ant Colony Optimization Algorithm, Chaos Optimization, Collaborative Optimization Algorithm, Parameter Optimization, Traveling Salesman Problem

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