Search - local search matlab

Search - local search matlab - List

[AI-NN-PR] ReversibleJumpMCMCSimulatedAnneaing DL : 0: This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
Date : 2025-12-20 Size : 936kb User : 大辉
[AI-NN-PR] iga DL : 0: 应用遗传算法是被认为求解NP难题的有效手段之一,求解物流配送车辆路径优化问题时,在传统遗传算法的基础上,并引入了免疫算法的思想,实验结果表明该算法具有更好的全局和局部搜索能力和收敛速度,可有效地解决物流配送车辆路径优化问题。-Application of genetic algorithms to solve NP is considered an effective means of problem solving to optimize logistics and distribution vehicle routing problem, in the traditional genetic algorithm based on immune algorithm and the introduction of ideas, experimental results show that the algorithm has a better overall and local search ability and convergence speed, which can effectively solve the logistics and distribution VRP.
Date : 2025-12-20 Size : 7kb User : 王博文
[AI-NN-PR] pso_pathplanning DL : 0: 粒子群(PSO)路径规划。这个是一个局部路径规划，用了深度优先搜索算法，可以走出“陷阱”。-Particle Swarm (PSO) path planning. This is a local path planning, with a depth-first search algorithm, we can get out a
Date : 2025-12-20 Size : 9kb User : 陈建胜
[AI-NN-PR] Function optimization algorithm DL : 0: 遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化，而不针对参数本身，从而不受函数约束条件的限制；搜索过程从问题解的一个集合开始，而不是单个个体，具有隐含并行搜索特性，可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework for solving nonlinear programming, which does not depend on the specific problem domain. The advantage of genetic algorithm is that the problem parameters are encoded into chromosomes for optimization, rather than the parameters themselves. The search process starts from a set of problem solutions, rather than a single individual, and has the implicit parallel search feature, which can greatly reduce the possibility of falling into the local minimum. Moreover, the algorithm does not rely on gradient information and does not require the objective function to be continuous and differentiable, which makes it suitable for solving large-scale and nonlinear combinatorial optimization problems that are difficult to be solved by traditional search methods.)
Date : 2025-12-20 Size : 33kb User : FZenjoys