A new optimization algorithm based on improved simulated annealing and genetic algorithm
Huawei Yi
Abstract
Huawei Yi
Abstract
A new Genetic Algorithm was proposed based on the analysis of the advantages and disadvantages of the Genetic Algorithm and Simulated Annealing Algorithm. The genetic algorithm with optimum reservation strategy was served as the main flow of the new algorithm which combined the mechanism of improved simulated annealing. In order to get the global optimum solution, the improved simulated annealing took the double threshold value and kept the middle optimum solution to reduce the computing capacity and enhanced the convergence speed. Through the simulation test of a series of typical functions, the result indicated that the new algorithm can improve the convergence speed and the ability of jumping out the local optimum solution greatly.
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A new Genetic Algorithm was proposed based on the analysis of the advantages and disadvantages of the Genetic Algorithm and Simulated Annealing Algorithm. The genetic algorithm with optimum reservation strategy was served as the main flow of the new algorithm which combined the mechanism of improved simulated annealing. In order to get the global optimum solution, the improved simulated annealing took the double threshold value and kept the middle optimum solution to reduce the computing capacity and enhanced the convergence speed. Through the simulation test of a series of typical functions, the result indicated that the new algorithm can improve the convergence speed and the ability of jumping out the local optimum solution greatly.
Key concepts: Simulated annealing, Algorithm, Adaptive simulated annealing, Computer science, Genetic algorithm, Population-based incremental learning, Convergence (economics), Reservation