为有效解决粒子群算法在求解路网问题时易陷入局部最优的缺点,根据高校地理数据和多核系统并行处理方式,将自平衡策略和变异思想结合且并行化,提出一种并行求解高校路网问题的正序变异的混合PSO算法。该算法引入适合此问题的自平衡正序...为有效解决粒子群算法在求解路网问题时易陷入局部最优的缺点,根据高校地理数据和多核系统并行处理方式,将自平衡策略和变异思想结合且并行化,提出一种并行求解高校路网问题的正序变异的混合PSO算法。该算法引入适合此问题的自平衡正序变异策略且采用并行处理方式,使其生成相互独立子群体且并行求解,来提高算法求解精度,保证算法多样性及收敛,降低计算时间。实验以Visual Studio 2005中C++编程实现仿真,结果表明此算法不但能有效求解高校路网问题,而且比离散PSO算法、并行自平衡PSO算法的解更优。展开更多
针对标准粒子群算法在求解路网问题时显现出易陷入局部极值的问题,根据高校地理数据,提出一种求解高校路网的逆序变异的新混合PSO算法。为平衡算法的全局和局部搜索能力及增强种群多样性,将一种自平衡策略作为变异条件,在产生新的群体...针对标准粒子群算法在求解路网问题时显现出易陷入局部极值的问题,根据高校地理数据,提出一种求解高校路网的逆序变异的新混合PSO算法。为平衡算法的全局和局部搜索能力及增强种群多样性,将一种自平衡策略作为变异条件,在产生新的群体中按照逆序变异率算子对粒子进行位置变异,从而使得粒子摆脱局部极值后继续进行迭代更新操作。以Visual Studio 2005中C++编程实现实验仿真,结果表明此算法不但能有效求解高校路网问题,而且新算法收敛精度高,有效克服了早熟收敛问题。展开更多
针对传统的可靠性建模方法难以建立复杂机电产品的可靠性数学模型,提出一种结合功能分解(FMA,function-motion-action)和故障树(FTA,fault tree analysis)的建模方法,降低了复杂机电产品的可靠性优化模型的构造难度。针对遗传算法(GA,ge...针对传统的可靠性建模方法难以建立复杂机电产品的可靠性数学模型,提出一种结合功能分解(FMA,function-motion-action)和故障树(FTA,fault tree analysis)的建模方法,降低了复杂机电产品的可靠性优化模型的构造难度。针对遗传算法(GA,genetic algorithm)和粒子群算法(PSO,particle swarm optimization)在模型求解时存在的不足,提出构建混合GA-PSO算法来改善GA算法易陷入局部最优或全局搜索能力弱的现象。通过数控磨齿机的实例分析,验证了用混合GA-PSO算法构造优化模型的可行性,以及采用混合粒子群算法优化求解的有效性。展开更多
A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems....A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.展开更多
Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. T...Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.展开更多
文摘为有效解决粒子群算法在求解路网问题时易陷入局部最优的缺点,根据高校地理数据和多核系统并行处理方式,将自平衡策略和变异思想结合且并行化,提出一种并行求解高校路网问题的正序变异的混合PSO算法。该算法引入适合此问题的自平衡正序变异策略且采用并行处理方式,使其生成相互独立子群体且并行求解,来提高算法求解精度,保证算法多样性及收敛,降低计算时间。实验以Visual Studio 2005中C++编程实现仿真,结果表明此算法不但能有效求解高校路网问题,而且比离散PSO算法、并行自平衡PSO算法的解更优。
文摘针对标准粒子群算法在求解路网问题时显现出易陷入局部极值的问题,根据高校地理数据,提出一种求解高校路网的逆序变异的新混合PSO算法。为平衡算法的全局和局部搜索能力及增强种群多样性,将一种自平衡策略作为变异条件,在产生新的群体中按照逆序变异率算子对粒子进行位置变异,从而使得粒子摆脱局部极值后继续进行迭代更新操作。以Visual Studio 2005中C++编程实现实验仿真,结果表明此算法不但能有效求解高校路网问题,而且新算法收敛精度高,有效克服了早熟收敛问题。
基金Projects(50275150,61173052) supported by the National Natural Science Foundation of ChinaProject(14FJ3112) supported by the Planned Science and Technology of Hunan Province,ChinaProject(14B033) supported by Scientific Research Fund Education Department of Hunan Province,China
文摘A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
文摘Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.
