Open Access

ARTICLE

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Prachi Agrawal¹, Khalid Alnowibet², Ali Wagdy Mohamed^3,4,*

1 Yogananda School of Artificial Intelligence, Computers & Data Science, Shoolini University, Solan, 173229, India
2 Statistics and Operations Research Department, College of Science, King Saud University, Riyadh, 11451, Kingdom of Saudi Arabia
3 Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
4 Department of Mathematics and Actuarial Science, School of Science and Engineering, The American University in Cairo, Egypt

* Corresponding Author: Ali Wagdy Mohamed. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 71(2), 2847-2868. https://doi.org/10.32604/cmc.2022.023126

Received 29 August 2021; Accepted 30 September 2021; Issue published 07 December 2021

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.

---

Keywords

---