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

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