Solving stochastic programming problems using modified differential evolution algorithms

Stochastic (or probabilistic) programming (SP) is an optimization technique in which the constraints and/or the objective function of an optimization problem contain random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, two modified differential evolution (DE) algorithms namely, LDE1 and LDE2 are used for solving SP problems. Two models of SP problems are considered; Stochastic Fractional Programming Problems andMultiobjective Stochastic Linear Programming Problems. The numerical results obtained by the LDE algorithms are compared with the results of basic DE, basic particle swarm optimization (PSO) and the available results from where it is observed that the LDE algorithms significantly improve the quality of solution of the considered problem in comparison with the quoted results in the literature.