Abstract
Asynchronous massively parallel genetic algorithm (AMPGA) based on a newly proposed society was implemented for relatively large scale optimization problems; and its parallel processing efficiency was evaluated using several optimization problems such as shape optimization and traveling salesman problems in this paper. Because of its asynchronous characteristics produced by the artificial society model which is an agent-oriented model, an emergent behavior such as timings of mating for crossover/mutation and even intergenerational crossover was also observed through shape optimization problems. For traveling salesman problem, large population model was also included in the proposed AMPGA and the effect of the number of agents on each processor was observed in this study. As a result, two types of shape optimization problems and 105-city traveling salesman problem were tested on CRAY T3D massively parallel processor system using the proposed method, and its applicability and parallel processing efficiency were verified.
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