【摘要】 随机机会约束规划作为一类重要的随机规划,广泛存在于许多领域中。为了寻找更有效的求解随机机会约束规划的算法,通过采用随机模拟来逼近随机函数,并在微粒群算法PSO(Particle Swarm Optimization)中利用随机模拟实现估计适应值和检验解的可行性,从而给出了求解随机机会约束规划的新算法,最后,测试其性能并与遗传算法进行了比较,实例结果表明该算法的正确性和有效性。更多还原

【Abstract】 Stochastic chance-constrained programming widely exits in different fields as a certain kind of important stochastic programming.In order to find an algorithm which can more effectively solve this programming problem,random simulation is adopted to approximate the stochastic function,and random simulation is used to realise estimating the fitness value in particle swarm optimization and for the purpose of checking the feasibility of the solution,thereby a new algorithm for stochastic chance-constrained programming is presented accordingly.Finally,the performance of the proposed algorithm is tested and compared with genetic algorithm,experimental results illustrate the correctness and the validity of it.更多还原