Study of the global optimization by adaptive SAGA
Guoli Li, Yican Wu
Abstract
Guoli Li, Yican Wu
Abstract
The genetic algorithm is based on the rules of probability transformation and has high ability in global optimization in searching the minimum in the possible solutions to the given problem,but the speed of convergence is lower. As for the simulated annealing algorithm, convergence to the global minimum can be achieved theoretically if the calculation time is long enough,but the result of global optimization is not ideal in practice because of the restriction of calculation speed and time. In this paper,a hybrid adaptive genetic algorithm,which is the combination of the simulated annealing algorithm and the genetic algorithm,is presented to enhance the speed of calculation and improve the global optimization.
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The genetic algorithm is based on the rules of probability transformation and has high ability in global optimization in searching the minimum in the possible solutions to the given problem,but the speed of convergence is lower. As for the simulated annealing algorithm, convergence to the global minimum can be achieved theoretically if the calculation time is long enough,but the result of global optimization is not ideal in practice because of the restriction of calculation speed and time. In this paper,a hybrid adaptive genetic algorithm,which is the combination of the simulated annealing algorithm and the genetic algorithm,is presented to enhance the speed of calculation and improve the global optimization.
Key concepts: Simulated annealing, Global optimization, Adaptive simulated annealing, Mathematical optimization, Meta-optimization, Convergence (economics), Genetic algorithm, Computer science