水电站水库优化调度的改进粒子群算法

A modified particle swarm optimizer for optimal operation of hydropower station

张双虎 黄强 吴洪寿 杨菊香

粒子群优化算法是通过粒子记忆、追随当前最优粒子，并不断更新自己的位置和速度来寻找问题的最优解。为了克服标准粒子群算法存在着早熟收敛、难以处理问题约束条件等缺点，本研究对递减惯性权值进行了改进，将其表示为粒子群进化速度与群体平均适应度方差的函数；给出了适合PSO算法的约束处理机制，提出了一种改进自适应粒子群算法，并将其应用于水库优化调度中。实例计算并与经典方法相比，表明该方法原理简单、易编程实现，能以较快的速度收敛于全局最优解。[著者文摘]

Particle swarm optimizer（PSO） searches the best solution of a problem by remembering and following the excellent particle and updating own position and speed continuously. In order to overcome the defect of premature and difficulty of dealing with constraint, this paper presents a modified adaptive PSO （MAPSO） which expresses inertia weight in a function determined by the evolution speed and the fitness variance of particle swarms and proposes a constraint handling strategy suit for PSO. A hydropower station operation demonstrates the successful application of the modified adaptive PSO. Study results show that the MAPSO is a simple, programming easily optimal algorithm and can find the global optimum solution quickly compared with traditional method.[著者文摘]

改进的粒子群算法

Improved particle swarm optimization

张建科[1] 刘三阳[2] 张晓清[3]

为改善基本粒子群算法的搜索性能,针对粒子群算法随机性较强、收敛较慢的问题,利用数学中的外推技巧给出了两个新的粒子位置更新公式,由此构造出一种新的算法——强引导型粒子群算法。新算法对粒子位置更新加以引导,试图减少算法的随机性以提高搜索效率。用4个基准函数对新算法进行试验,结果表明,新算法在稳定性和收敛性上优于基本粒子群算法。[著者文摘]

To improve the searching performance ofparticle swarm optimization, in accordance with problems ofthe too strong stochastic characteristic and slow convergence speed of the traditional particle swarm optimization. Two new position update equations are proposed by using the strategy ofextrapolation in mathematics. Thus, a new class ofparticle swarm optimization with induction-enhanced is given. The induction is made to updating particle＇s position by the new arithmetic in order to decreasing the randomicity of particle swarm optimization and improve the efficiency of searching. Four benchmarks function are tested and show that the new algorithm is better than the traditional particle swarm optimization with both a better stability and a steady convergence.[著者文摘]

基于粒子群算法的多目标车辆调度模型求解

Solution of Multi-Objective Vehicle Scheduling Model Based on Particle Swarm Optimization

丰伟 李雪芹

车辆调度问题是具有复杂约束条件的组合优化问题,在理论上属NP-hard问题.考虑车辆数目最少和车辆运行时间最短,建立了具有时间约束的多目标车辆调度模型.并采用粒子群算法（PSO）求解车辆调度问题,以寻求最优车辆调度方案.在实例中通过运用粒子群算法和遗传算法进行比较分析,结果表明,PSO算法简单可行,在优化性能、收敛速度及鲁棒性等方面优于遗传算法,能较好地解决组合优化问题.[著者文摘]

Vehicle scheduling is the combinational optimization problem that has complex restricted conditions. It belongs to the NP-hard problem theoretically. A multi-objective vehicle scheduling model based on te time constraints was established by considering the minimal vehicle number and the shortest vehicle runtime. Based on this model, a particle swarm optimization （PSO） based scheduling algorithm was proposed to obtain the optimum vehicle scheduling ,scheme. A scheduling example was resolved by the PSO and generic algorithm （GA）. The result shows that the proposed algorithm is simple and feasible, with better performances in optimization, convergence speed and robustness compared with GA.[著者文摘]

求解非线性方程组的混沌粒子群算法及应用

Chaos particle swarm optimization algorithm for solving systems of nonlinear equations and it＇s application

莫愿斌 陈德钊 胡上序

针对非线性方程组的求解在工程上具有广泛的实际意义，经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题，提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法，使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向，以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息，并反映到逐代更新的个体极值和群体极值中，可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。[著者文摘]

Aimed at the widespread practical significance of solving nonlinear equations in engineering, classic numerical methods such as Newton method are highly sensitive to the initial guess, but it is diffi- cult to find a suitable good initial guess in practical operation, chaos particle swarm optimization （CPSO） algorithm was proposed to solve nonlinear equations. It introduced chaos to particle swarm optimization （PSO）, making every particle selected a suitable search direction from PSO search mechanism and chaos search mechanism. By virtue of chaos＇s ergodicity, it enhanced every particle with search property and made every particle could comprehensively get the useful information about the objective function, which was reflected in present global optimal point and the optimal point of each particle in every iteration. By this, CPSO could more effectively adjust search direction of each particle and finally got the global optimal point of the problem. Experimental results show the effectiveness of the proposed tactics is obvious. At last the algorithm was applied to model relations between composite structures fatigue life with stress, temperature and moisture.[著者文摘]

基于遗传粒子群算法的集成块布局优化

Allocation Optimization of Hydraulic Manifold Blocks Based on Genetic Particle Swarm Algorithm

王占奎[1] 焦红伟[2] 王得胜[3] 逄明华[1]

根据集成块的结构特点提出了一种集成块布局的优化方法,建立了集成块空间布局优化设计的数学模型和集成块的参数化描述,并结合遗传粒子群算法对提出的数学模型进行了求解。计算实例表明,运用遗传粒子群算法优化该数学模型可以很好地完成集成块的布局优化设计,为以后集成块的自动优化设计打下了坚实的基础。[著者文摘]

An optimization method of allocation plan about hydraulic manifold blocks was put forward according to the structure features of hydraulic manifold blocks. A mathematic model of optimization designing method of allocation plan which was solved by genetic particle swarm algorithm was built and a method of manifold block parameterized description was put forward. The solving example indicates the problem of optimization design of allocation plan about hydraulic manifold blocks can be well solved by using genetic particle swarm algorithm.[著者文摘]

基于混合粒子群算法的液压弯辊控制

Hydraulic Bending Roll Control Based on Mixed Particle Swarm Optimizer

王京 李洪全

在带钢轧制过程中，由于液压弯辊伺服控制系统是非线性时变系统，并受多种因素的影响，很难建立精确的数学模型，弯辊也需要对轧制力的变化做出快速的反应，因此控制器的性能是关键因素，本文将粒子群算法和遗传算法相结合，组成新的混合算法并将其应用到某液压弯辊伺服系统的自适应PID控制中，并对此进行仿真研究，取得了较好的控制效果。[著者文摘]

Hydraulic bending roll system is non-linear, time-variant and disturbed by many factors. It＇s difficult to establish a precise mathematical model. Bending rolls need to make fast response to the change of rolling force. The performance of controller is the key. A new modified algorithm which composites of generic algorithm （GA） and particle swarm optimizer （PSO） was proposed and was applied in the adaptive PID control of hydraulic bending system. A simulation model was built and a better control performance was got.[著者文摘]