Gaining-sharing knowledge based algorithm for solving stochastic programming problems

Funding Number

RSP-2021/305

Funding Sponsor

King Saud University

Author's Department

Mathematics & Actuarial Science Department

Find in your Library

https://doi.org/10.32604/cmc.2022.023126

All Authors

Prachi Agrawal, Khalid Alnowibet, and Ali Wagdy Mohamed

Document Type

Research Article

Publication Title

Computers, Materials and Continua

Publication Date

1-1-2022

doi

10.32604/cmc.2022.023126

Abstract

This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and the results are compared with eight other state-of-the-art metaheuristic algorithms. The obtained results are also compared with the optimal global solution and the results quoted in the literature. To investigate the performance of the GSK algorithm on a real-world problem, a solid stochastic fixed charge transportation problem is examined, in which the parameters of the problem are considered as random variables. The obtained results show that the GSK algorithm outperforms other algorithms in terms of convergence, robustness, computational time, and quality of obtained solutions.

First Page

2847

Last Page

2868

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