Highly Parallel Simulated Annealing Applied to the Optimization of Structural Systems

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Simulated annealing is one of the probabilistic optimization methods for solving complicated optimization problems, and it is started to be used in real world problems recently. However, the method requires many repeated calculations and hence its computational cost becomes very high. Therefore, it is very important and urgent to parallelize the optimization method. Our research is focused on the parallelization of simulated annealing and the development of effective parallel algorithms for parallel simulated annealing. The followings are the conclusions.1) The extension of Temperature Parallel Simulated Annealing to continuous optimization problems is proposed and it provides effective performance.2) The neighborhood range can be automatically adjusted by the proposed method based on the constant probability of acceptance.3) The maximum temperature of the temperature schedule in simulated annealing for continuous optimization problems can be automatically determined by the proposed method where the temperature increases from the lowest temperature instead of cooling.4) A new method for determination of the appropriate neighborhood range based on the parallel annealing with different neighborhood ranges is proposed, and it yields good search performance.