Abstract
A new Heuristic Genetic Information based Ant Colony Genetic Algorithm (HGI-ACGA) to solve Traveling Salesman Problems(TSP) was proposed. HGI-ACGA's two sub-algorithms are Ant Colony Genetic Algorithm(ACGA) and Heuristic Genetic Information based Ant Colony Algorithm(HGI-ACA). ACGA enhances genetic algorithm's population diversity and reduces the search domain, while HGI-ACA eliminates the creation of invalid tours and also avoids depending on pheromone excessively. A combination of genetic information and pheromone leads to a significant improvement in performance. The strategy of HGI-ACGA algorithm can improve convergence rate and capacity of searching optimal solution. Experimental results show that the proposed algorithm generally exhibits a better solution and a higher rate of convergence for TSP than ACGA and ACA.
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