Abstract
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 and Multiobjective 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
