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In the paper, a new approach to exploiting parallelism in ant colony optimization (ACO) is implemented on a supercomputer (Cray T3E). Unlike the previous methods where results were based on either simulation or independent executions, in this paper based on the implementation we have studied the issues of parallelization and the overhead of communications apart from the idle times required in case of synchronous communication. The results are compared with already available methods. Moreover, by varying the values of different parameters, the effects are also analyzed for this method. Albeit the optimization method being general, TSP (Traveling Salesman Problem) is chosen for experimentation as it is widely researched and standard benchmarks are also available. The communication interval balances the total communication time and the frequency of global update. At the same time the best ant in each colony alone is allowed to update globally, even though locally all ants update the pheromone trails. The results obtained convince the efficiency of the approach.
Date of Conference: 2002