Xiamen Key Laboratory of Application Specific Integrated Circuit System

Xiamen, China

Xiamen Key Laboratory of Application Specific Integrated Circuit System

Xiamen, China

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Fu W.,Huaqiao University | Fu W.,Xiamen Key Laboratory of Application Specific Integrated Circuit System | Ling C.,Huaqiao University | Ling C.,Xiamen Key Laboratory of Application Specific Integrated Circuit System
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | Year: 2013

A new adaptive iterative chaos optimization method is proposed to improve the problems that the optimal results generated from the existing chaotic optimization methods rely on initial points and that search efficiency of these methods is lower. It is proved that the chaotic map has no rational number fixed point, then the mapping relational formula is used to establish a chaotic model that is used to solve the Lyapunov exponent, and the sensitivity of chaotic maps to initial values is investigated under large variation and small variation on initial starting points. The chaotic map is then used to establish chaotic generator to replace the finite-collapse map, and to improve the dynamic performance of chaotic optimization. The method improves the search efficiency by continuously reducing the searching space of variables and enhancing search precision. Numerical results show that the optimal results generated by the proposed method do not depend on the initial value, and the search efficiency is high. Comparisons with the Logistic mapping and the Tent mapping optimization method show that the average search efficiency of the proposed method improves about 71.6% and 62.6%, respectively.


Fu W.-Y.,Huaqiao University | Fu W.-Y.,Xiamen Key Laboratory of Application Specific Integrated Circuit System | Ling C.-D.,Huaqiao University | Ling C.-D.,Xiamen Key Laboratory of Application Specific Integrated Circuit System
Jisuanji Xuebao/Chinese Journal of Computers | Year: 2014

A new intelligent heuristic algorithm which is a combination of the Brownian motion and simulated annealing measure is proposed to improve the search efficiency of traditional algorithm. The algorithm has established a connection between Brownian particle motion time and simulated annealing temperature. Reciprocal value of annealing temperature is equivalent to Brownian particle motion time. A neighborhood function model based on the Brownian motion and the corresponding temperature of descent function are obtained through the theory analysis. The proposed annealing temperature of descent function can be fast, and it has a high efficiency. The numerical results show that the algorithm is fast, stable and easy to realize and can improve significantly the computational efficiency for solving the global optimization problem.

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