Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

作     者：Prachi Agrawal Khalid Alnowibet Ali Wagdy Mohamed 

作者机构：Yogananda School of Artificial IntelligenceComputers&Data ScienceShoolini UniversitySolan173229India Statistics and Operations Research DepartmentCollege of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Operations Research DepartmentFaculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial ScienceSchool of Science and EngineeringThe American University in CairoEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第5期

页      面：2847-2868页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：The research is funded by Researchers Supporting Program at King Saud University (Project#RSP-2021/305) 

主　　题：Gaining-sharing knowledge based algorithm metaheuristic algorithms stochastic programming stochastic transportation problem 

摘      要：This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.