Hybrid genetic-SPSA algorithm based on random fuzzy simulation for chance-constrained programming

In this paper, hybrid genetic-SPSA algorithm based on random fuzzy simulation is proposed for solving chance-constrained programming in random fuzzy decision-making systems by combining random fuzzy simulation, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA). In the provided algorithm, random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable, CA is employed to search for the optimal solution in the entire space, and SPSA is used to improve the new chromosomes obtained by crossover and mutation operations at each generation in CA. At the end of this paper, an example is given to illustrate the effectiveness of the presented algorithm.