2007Control theory & applicationsRequires access

Chaos annealing searching algorithm based on power function carrier

GU Sheng-na

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Abstract

Chaos searching strategy for combination optimization problem is proposed. In the process of optimization, the operation position of the solution matrix can be determined by chaos search, which makes the algorithm search the optimization result in the legal solution space. The power function carrier is adopted to improve the ergodicity and the sufficiency of the chaos optimization, and the simulated annealing is implemented to improve the optimization effect, therefore, the algorithm can get rid of the local minimum and reach the global minimum. The algorithm can be applied to solve many actual engineering problems. The simulation results prove the validity.

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What this paper is about

Chaos searching strategy for combination optimization problem is proposed. In the process of optimization, the operation position of the solution matrix can be determined by chaos search, which makes the algorithm search the optimization result in the legal solution space. The power function carrier is adopted to improve the ergodicity and the sufficiency of the chaos optimization, and the simulated annealing is implemented to improve the optimization effect, therefore, the algorithm can get rid of the local minimum and reach the global minimum. The algorithm can be applied to solve many actual engineering problems. The simulation results prove the validity.

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Available abstract

Chaos searching strategy for combination optimization problem is proposed. In the process of optimization, the operation position of the solution matrix can be determined by chaos search, which makes the algorithm search the optimization result in the legal solution space. The power function carrier is adopted to improve the ergodicity and the sufficiency of the chaos optimization, and the simulated annealing is implemented to improve the optimization effect, therefore, the algorithm can get rid of the local minimum and reach the global minimum. The algorithm can be applied to solve many actual engineering problems. The simulation results prove the validity.

Key concepts: Simulated annealing, Ergodicity, Mathematical optimization, CHAOS (operating system), Local optimum, Optimization problem, Position (finance), Algorithm

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