Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem

Genetic Algorithm and Simulated Annealing: A Combined Intelligent Optimization Method and Its Application to Subsynchronous Damping Control in Electrical Power Transmission Systems

“…Here, we provide the solution by combining genetic algorithm (GA) and simulated annealing (SA), termed GA-SA. As a hybrid and global optimization strategy, GA-SA takes advantage of both GA's parallel-searching capability and SA's probabilistic jumping property [14][15][16][17]: The fast and global parallel searching ability of GA is retained, and the diversity is improved by SA state transition. Thus, premature convergence in GA can be avoided.…”

“…Thanks to the powerful global searching ability, the criteria for the selection of algorithm parameters are very much relaxed, resulting in improved performance and robust optimization. Since the basic principle of GA-SA has been well documented [14][15][16][17], only the particular procedures employed to design the SSR control (i.e., SEDC and SVC-SSDC) are elaborated in the following.…”

Genetic Algorithms Application to Electric Power Systems

“…Different hybrid algorithms, used to solve the UCP, are available in the literature [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. These algorithms consist of two or more of the following methods: Classical Optimization (e.g.…”

Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

“…The proposed algorithm is the extension of the work carried out by Mantawy et al (1999). They have proposed the hybrid GTS algorithm integrating GA, TS, and SA algorithms.…”