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In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.

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This paper presents a novel metaheuristic approach called ACOMAC algorithm for solving the TSP (traveling salesman problem). We introduce multiple ant clans' concept from parallel genetic algorithm to search solutions space that using different islands to avoid local minima and so as to obtain global minimum for solving the traveling salesman problem. In addition, we present two methods called multiple nearest neighbor (NN) and dual nearest neighbor (DNN) to ACOMAC to improve large TSPs thus obtain good solutions quickly. According to our simulation results, the ACOMAC outperforms the ant colony system (ACS) in average length comparison of traveling salesman problem. In this work, it is observed that ACOMAC or ACS adding DNN or NN approach as initial solutions can provide a significantly improvement for obtaining a global optimum solution or a near global optimum solution in large TSPs.0-7803-7282-4/02/%10.00 02002 IEEE

In this pqer, we present an eflcient metaheuristic method for solving the problem of TSP (traveling salesman problem). We consider mult@le ant clans' concept fiom parallel genetic algorithm to search solutions space that using direrent islands to avoid local minima and so as to obtain global minimum for solving the TSP problem. OUT simulation results indicate that the proposed novel TSP method (called ACOUAC algorithm) performs better than the ant coloty system (ACS).