Multistage decision-making using simulated annealing applied to a fuzzy automaton

Abstract A new optimization method that stochastically builds up a solution step-by-step in combination with simulated annealing is used for multistage decision-making of finite-state automaton. The quality of the new algorithm for larger scale problems was tested by two tasks: (1) maximizing the probability of goal satisfaction with fuzzy goals subject to fuzzy constraints and (2) minimizing the length of decision sequence leading to a specified termination state. The new method required a number of evaluations of solutions, which was smaller by orders of magnitude in comparison with a “classical” genetic algorithm.

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