Solving Multi Objective Stochastic Programming Problems Using Differential Evolution

被引：0

作者：

Thangaraj, Radha ^{[1
]}

Pant, Millie ^{[2
]}

Bouvry, Pascal ^{[1
]}

Abraham, Ajith ^{[3
]}

机构：

[1] Univ Luxembourg, Fac Sci Technol & Commun, Luxembourg, Luxembourg

[2] Indian Inst Technol Roorkee, Dept Paper Technol, Roorkee, Uttar Pradesh, India

[3] MIR Labs, Sci Network Innovat & Res Excellence, Boston, MA USA

来源：

SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING | 2010年 / 6466卷

关键词：

Differential Evolution; stochastic programming; multiobjective optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：54 / +

页数：2

共 12 条

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