Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with ...Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.展开更多
In order to avoid such problems as low convergent speed and local optimalsolution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In thisalgorithm, a mutative scale chaos optimization strateg...In order to avoid such problems as low convergent speed and local optimalsolution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In thisalgorithm, a mutative scale chaos optimization strategy is operated on the population after agenetic operation. And according to the searching process, the searching space of the optimalvariables is gradually diminished and the regulating coefficient of the secondary searching processis gradually changed which will lead to the quick evolution of the population. The algorithm hassuch advantages as fast search, precise results and convenient using etc. The simulation resultsshow that the performance of the method is better than that of simple genetic algorithms.展开更多
This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high effic...This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.展开更多
In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of produc...In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.展开更多
基金Hunan Provincial Natural Science Foundation of China (No. 06JJ50103)the National Natural Science Foundationof China (No. 60375001)
文摘Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.
文摘In order to avoid such problems as low convergent speed and local optimalsolution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In thisalgorithm, a mutative scale chaos optimization strategy is operated on the population after agenetic operation. And according to the searching process, the searching space of the optimalvariables is gradually diminished and the regulating coefficient of the secondary searching processis gradually changed which will lead to the quick evolution of the population. The algorithm hassuch advantages as fast search, precise results and convenient using etc. The simulation resultsshow that the performance of the method is better than that of simple genetic algorithms.
基金Project supported by National High-Technology Research and De-velopment Program of China (Grant No .863-2001AA602021)
文摘This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.
基金Project(2012BAK09B02-05) supported by the National "Twelfth Five-year" Science & Technology Support Plan of China
文摘In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.
