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[Algorithm] FDRPSO DL : 0: code for fitness distance ratio PSO
Date : 2025-12-26 Size : 4kb User : Samrat
[Algorithm] jifjsaf DL : 0: Particle swarm optimization algorithm, also known as particle swarm optimization algorithm (Swarm Optimization Particle), abbreviated as PSO, is a parallel algorithm on the basis of the observation of the behavior of animal clusters, the use of groups of individuals in the information sharing to make the whole group of movement in the problem solving space generated the disorder to the orderly evolution process, thereby obtaining the optimal solution. Particle swarm optimization algorithm and simulated annealing algorithm, it is also the random solution, through the iterative search for the optimal solution, it is through the fitness to uate the quality of the solution, but it is more simple than the genetic algorithm rules, it does not have the genetic algorithm crossover (Crossover) and Mutation operation, it follows the current search to the optimal value to find the global optimal.-Particle swarm optimization algorithm, also known as particle swarm optimization algorithm (Swarm Optimization Particle), abbreviated as PSO, is a parallel algorithm on the basis of the observation of the behavior of animal clusters, the use of groups of individuals in the information sharing to make the whole group of movement in the problem solving space generated the disorder to the orderly evolution process, thereby obtaining the optimal solution. Particle swarm optimization algorithm and simulated annealing algorithm, it is also the random solution, through the iterative search for the optimal solution, it is through the fitness to uate the quality of the solution, but it is more simple than the genetic algorithm rules, it does not have the genetic algorithm crossover (Crossover) and Mutation operation, it follows the current search to the optimal value to find the global optimal.
Date : 2025-12-26 Size : 1kb User : jianglantian
[Algorithm] manwandiaodu-PSO DL : 0: 群微粒算法：本算法用群微粒算法求目标函数的最大值 //本算法使用步骤 // （1）派生自己的群微粒类，类中必须定义double GetFit(PARTICLE&)方法，用来计算每个微粒的适合度 // （2）生成派生类实例，并在构造函数中指明微粒坐标维数和群体个数 // （2）设置微粒坐标上界数组和下界数组，并用SetXup与SetXdown设置微粒坐标上下界 // （3）用SetVmax方法设置微粒最大速度 // （4）设置可选参数：C1，C2，W和通讯函数 // （5）采用Run方法进行优化运算，优化后用GetBest方法获得最优个体适合度和坐标-Group of particle algorithm: this algorithm with a group of particles for a maximum of the objective function //the algorithm using steps //derived their own group of particles (1) class, the class must be defined in a double GetFit (PARTICLE &) method, is used to calculate the fitness of each PARTICLE //(2) generates derived class instance, and indicate the particles in the constructor coordinate dimension and the number of groups //(2) set the particle coordinate arrays and lower bound, the upper bound and SetXup and SetXdown set the upper and lower bounds particle coordinates //(3) set maximum speed particles in SetVmax way //(4) set the optional parameters: C1 and C2, W and communications functions //(5) Run method is adopted to optimize arithmetic, the optimized GetBest method to obtain the optimal individual fitness and coordinates
Date : 2025-12-26 Size : 289kb User : ethbk
[Algorithm] pso DL : 0: 粒子群算法求解非线性函数，包含算法程序、函数等，生成最优适应度解。-Particle swarm algorithm for solving nonlinear functions, including arithmetic procedures, functions, etc., generate optimal fitness solution.
Date : 2025-12-26 Size : 6kb User : 许可