Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems

Khalid Abdulaziz Alnowibet, Salem Mahdi, Mahmoud El-Alem, Mohamed Abdelawwad and Ali Wagdy Mohamed
Additional contact information
Khalid Abdulaziz Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Salem Mahdi: Educational Research and Development Center Sanaa, Sanaa 31220, Yemen
Mahmoud El-Alem: Department of Mathematics & Computer Science, Faculty of Science, Alexandria University, Alexandria 21544, Egypt
Mohamed Abdelawwad: Institute for Computer Architecture and System Programming, University of Kassel, 34127 Kassel, Germany
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Mathematics, 2022, vol. 10, issue 8, 1-25

Abstract: In this paper, a hybrid gradient simulated annealing algorithm is guided to solve the constrained optimization problem. In trying to solve constrained optimization problems using deterministic, stochastic optimization methods or hybridization between them, penalty function methods are the most popular approach due to their simplicity and ease of implementation. There are many approaches to handling the existence of the constraints in the constrained problem. The simulated-annealing algorithm (SA) is one of the most successful meta-heuristic strategies. On the other hand, the gradient method is the most inexpensive method among the deterministic methods. In previous literature, the hybrid gradient simulated annealing algorithm (GLMSA) has demonstrated efficiency and effectiveness to solve unconstrained optimization problems. In this paper, therefore, the GLMSA algorithm is generalized to solve the constrained optimization problems. Hence, a new approach penalty function is proposed to handle the existence of the constraints. The proposed approach penalty function is used to guide the hybrid gradient simulated annealing algorithm (GLMSA) to obtain a new algorithm (GHMSA) that finds the constrained optimization problem. The performance of the proposed algorithm is tested on several benchmark optimization test problems and some well-known engineering design problems with varying dimensions. Comprehensive comparisons against other methods in the literature are also presented. The results indicate that the proposed method is promising and competitive. The comparison results between the GHMSA and the other four state-Meta-heuristic algorithms indicate that the proposed GHMSA algorithm is competitive with, and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final result.

Keywords: nonlinear function; constrained optimization; hybrid algorithm; global optima; line search; gradient method; meta-heuristics; simulated annealing algorithm; constraint handling; penalty function; evolutionary computation; numerical comparisons (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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