Search - pso Constraint

Search - pso Constraint - List

[AI-NN-PR] newpso_withviolent DL : 0: 一种新型带有约束条件的标准粒子群算法，小弟第一次上传，还请各位大哥多多帮助和支持！-A new type of constraint condition with the standard particle swarm optimization,小弟From the first time, also please a lot of help and support older brother!
Date : 2026-01-11 Size : 2kb User : 毛毛
[AI-NN-PR] pso-reservoir DL : 1: 用粒子群算法求解单一水库优化调度，只需要修改相应的约束条件就可以进行优化计算了-With a single particle swarm algorithm is optimizing reservoir operation, only need to modify the corresponding constraint conditions can were optimized
Date : 2026-01-11 Size : 3kb User :
[AI-NN-PR] constrain-opt DL : 0: 针对工程优化设计问题,提出了基于混沌粒子群算法的工程约束优化问题求解方法。CPSO算法利用混沌搜索的全局遍历性、随机性和规律性等特点, 引导粒子在全局范围内搜索, 从而克服了传统粒子群算法早熟收敛的缺点。该算法以种群适应度方差作为粒子群优化算法早熟收敛的判据, 并用惩罚函数法处理违法约束的粒子, 当基本粒子群算法陷入早熟时, 随机选择粒子群中的部分粒子实施混沌搜索, 直至满足迭代收敛条件为止。CPSO算法能提高种群的多样性和粒子搜索的遍历性, 从而有效提高了PSO算法的收敛速度和精度。两个工程约束优化实例的求解结果表明,该算法的优化结果最好, 收敛速度也比较快-Based on engineering design optimization problems, and put forward based on chaotic particle swarm optimization algorithm of engineering problem solving methods. CPSO algorithm by using chaos search The global ergodicity, stochastic characteristics and regularity, and lead particles in the global scope search, and overcome the traditional particle swarm algorithm premature convergence faults. In this algorithm, the population fitness variance as the particle swarm optimization algorithm of the criterion of premature convergence, the penalty function method and deal with illegal constraint particles, when basic particle swarm to calculate Law in early maturity, random selection of particle swarm of these particles implementation chaotic search, until the convergence conditions meet so far. CPSO algorithm can improve the population Sample sex and particles of searching ergodicity, thus effectively improved PSO algorithm convergence speed and accuracy. Two engineering constraint optim
Date : 2026-01-11 Size : 728kb User : 吴胜亮
[AI-NN-PR] Another-Particle-Swarm-Toolbox DL : 0: 粒子群优化工具箱，采用Matlab编写的PSO程序工具箱-Development Notes for psopt toolbox Future plans (in no particular order): * Performance improvement: Automatically check for existence of constraints, skip boundary-checking if unconstrained. * Performance improvement: Automatically vectorize fitness functions supplied by user, if not already vectorized. * Performance improvement: Detect and eliminate dependent (i.e. redundant) linear constraints. * Performance improvement: Automatically choose "penalize" versus "soft" constraint handling method based on type of constraints in problem. If any equality constraints exist, using "penalize", otherwise default to "soft". If "soft", automatically switch to "penalize" if more than a given percentage of particles (initial or interim) are infeasible. * Feature: An output function defined by options.OutputFcns that can save current swarm state in a file which can be reloaded later as a problem structure, in case of a system crash. * Feature: Implement multiobjective optimization option
Date : 2026-01-11 Size : 43kb User : 张绍良