Evolutionary Algorithms for Solving Stochastic Programming Problems

Nature Inspired Optimization Algorithms (NIOA) are inspired by biological and sociological phenomena and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular NIOA, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving multi-objective stochastic programming problems. The numerical results obtained by PSO and DE are compared with the available results from where it is observed that the PSO and DE algorithms significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.