基于微分进化算子的量子粒子群优化算法及应用

分析了量子粒子群优化算法(Quantum-behaved Particle Swarm Optimization,QPSO)的进化方程,指出其存在的局部收敛问题,通过将微分进化(Differential Evolution,DE)的基本操作思想引入到QPSO中,提出了改进的QPSO算法(QPSO-DE);算法改进的方法是在粒子搜索过程中,以一定的概率对粒子的每一维执行微分进化操作,以增加粒子的随机性,从而减少了粒子群体因多样性缺失而易于陷入局部最优或停滞的情况,增强了粒子群体的搜索能力,提高了算法的优化性能;对多个标准测试函数及在IIR数字滤波器优化设计中的仿真实验结果表明,与PSO算法和QPSO算法相比,QPSO-DE算法能够取得更好的优化结果。     

灰色多属性决策的模糊互补判断矩阵排序方法

以灰色系统理论的思想和方法为基础,探讨了决策方案的属性值为区间灰数的灰色多属性决策问题,提出了解决这种灰色决策问题的决策方法.根据区间灰数的本质特征,首先定义了两区间灰数的新的相离度和构建了基于相离度的灰色区间关联系数公式与灰色区间相时关联系数公式;其次通过引入方案间优势度和优势度比较矩阵概念及其计算公式,证明了方案间优势度比较矩阵为模糊互补判断矩阵,从而给出了方案的排序.实例分析说明了所提出的灰色决策方法的合理性及其算法的有效性.     

基于改进粒子群算法的系统辨识新方法

提出了一种利用改进的粒子群优化算法对系统进行辩识的方法.该方法是将典型的数学模型的相互组合而构成系统模型的新辨识方法,即首先将系统结构辨识问题转化为组合优化问题,然后采用粒子群优化算法同时实现系统的结构辨识与参数辨识.为了进一步增强粒子群优化算法的辨识性能,提出了利用一种改进的粒子群优化算法.最后,给出了仿真示例,结果验证了所给的系统辨识方法的合理性和有效性.     

Hybrid particle swarm optimization for multiobjective resource allocation

Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.     

Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence

A fuzzy particle swarm optimization （PSO） on the basis of elite archiving is proposed for solving multi-objective optimization problems. First, a new perturbation operator is designed, and the concepts of fuzzy global best and fuzzy personal best are given on basis of the new operator. After that, particle updating equations are revised on the basis of the two new concepts to discourage the premature convergence and enlarge the potential search space; second, the elite archiving technique is used during the process of evolution, namely, the elite particles are introduced into the swarm, whereas the inferior particles are deleted. Therefore, the quality of the swarm is ensured. Finally, the convergence of this swarm is proved. The experimental results show that the nondominated solutions found by the proposed algorithm are uniformly distributed and widely spread along the Pareto front.     

基于改进粒子滤波的双站无源定位跟踪算法研究

在非线性非高斯状态空间下,粒子滤波器是一种有效的非线性滤波算法,它的关键问题包括粒子权重的计算、粒子重采样和状态估计等.根据粒子滤波算法思想和双站无源定位跟踪的非线性,将粒子滤波算法用于双站无源定位跟踪问题,给出了一种改进的粒子滤波算法,并对其关键问题根据双站无源定位跟踪的特殊性进行了改进.利用matlab进行了仿真实验,与最小二乘算法、扩展卡尔曼滤波算法进行了比较,结果表明所提算法定位跟踪精度优于其他方法.     

单机不同尺寸工件批调度问题的优化算法

研究了单机环境下不同尺寸工件的批调度问题,引入微粒群算法对制造跨度进行优化.首先给出了问题的微粒表达形式,并根据问题的离散优化特性对微粒状态的更新方法进行了改进;然后将微粒群算法和分批的启发式算法进行有效结合,改善近似解的质量.实验中对各类不同规模的算例均进行了仿真,结果表明了微粒群算法的有效性.     

基于新的采样更新方法的粒子滤波算法

以往的粒子滤波采用由初始先验概率密度产生一组粒子,然后通过重要性密度函数去更新粒子,但会产生粒子退化的问题,因此引入了各种各样的重采样算法,但这样做又产生了粒子多样性丧失的问题。针对粒子滤波的粒子退化现象,提出基于新的采样更新方法的粒子滤波算法,新方法从滤波值和滤波误差协方差矩阵上产生粒子。仿真试验表明,新方法在非线性非高斯情况下要远远好于EKF。     

嵌入隔离小生境技术的混沌粒子群算法

针对标准粒子群算法(standard particle swarm optimization,SPSO)无法很好平衡全局与局部搜索能力,且收敛速度较慢、易于早熟收敛等问题,提出了嵌入隔离小生境技术的混沌粒子群算法(isolation niches em-bedded in chaos particle swarm optimization,INCPSO)。利用隔离小生境技术,保证了解的多样性,同时,引入混沌搜索策略,提高了解的搜索精度和收敛速度,且避免早熟收敛。仿真试验结果表明,与标准粒子群算法和只嵌入隔离小生境技术的粒子群算法(isolation niches particle swarm optimization,INPSO)相比,嵌入隔离小生境技术的混沌粒子群算法对复杂问题的求解能力较强,寻优性能较好。     

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.