Open AccessSolving Multi Objective Stochastic Programming Problems Using Differential Evolution
Author(s) -
Radha Thangaraj,
Millie Pant,
Pascal Bouvry,
Ajith Abraham
Publication year - 2010
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/978-3-642-17563-3_7
Subject(s) - stochastic programming , mathematical optimization , computer science , stochastic optimization , probabilistic logic , differential evolution , linear programming , optimization problem , random variable , stochastic process , mathematics , artificial intelligence , statistics
Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains 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, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variants significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.
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