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

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.

A Family of Hybrid Stochastic Conjugate Gradient Algorithms for Local and Global Minimization Problems

“…When a stochastic method as a global optimization algorithm is combined with a globally convergent method (deterministic method), the result is a global optimization algorithm [55,56].…”

“…In many studies, the numerical outcomes indicated that the interbreed between a classical method and a random technique is very successful in overcoming the weakness of these methods. See [55][56][57][58][59]. Consequently, this part of the paper seeks to solve Problem (2).…”

“…Numerical outcomes demonstrated that the interbreed between classical and stochastic techniques has been hugely successful. See [55][56][57][58][59].…”

Leveraging Quantum Annealing for Ligand Modelling in Drug Discovery

“…Many classical algorithms such as simulated annealing are computationally expensive and can take a long time to converge to a solution [27] . Additionally, these algorithms are often limited in their ability to handle large and complex molecular structures, which are common in the eld of drug discovery [28] .…”

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

“…For engineering optimization problems, it is of great potential to pursue the optimal or the best solution to enhance diverse targets, such as production safety, efficiency, and energy consumption. However, current engineering optimization problems exhibit distinctive characteristics of multi-constraints, non-linearities, and multi-modalities, making it increasingly challenging for traditional optimization methods to handle such problems [2][3][4]. Hence, the research of effective optimization strategies for engineering optimization problems is always a hot spot.…”