本压缩包包含两部分：无约束优化问题和求解的遗传算法matlab源代码，用户也可用本程序求解其他无约束优化问题

http://www.mathworks.com/matlabcentral/fileexchange/25986-constrained-particle-swarm-optimization Description Previously titled "Another Particle Swarm Toolbox" Introduction Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals, such as flocks of birds, schools of fish, or swarms of locusts. The individual creatures, or "particles", in this algorithm are primitive, knowing only four simple things: 1 & 2) their own current location in the search space and fitness value, 3) their previous personal best location, and 4) the overall best location found by all the particles in the "swarm". There are no gradients or Hessians to calculate. Each particle continually adjusts its speed and trajectory in the search space based on this information, moving closer towards the global optimum with each iteration. As seen in nature, this computational swarm displays a remarkable level of coherence and coordination despite the simplicity of its individual particles. Ease of Use If you are already using the Genetic Algorithm (GA) included with MATLAB's Global Optimization Toolbox, then this PSO toolbox will save you a great deal of time. It can be called from the MATLAB command line using the same syntax as the GA, with some additional options specific to PSO. This will allow a high degree of code re-usability between the PSO toolbox and the GA toolbox. Certain GA-specific parameters such as cross-over and mutation functions will obviously not be applicable to the PSO algorithm. However, many of the commonly used options for the Genetic Algorithm Toolbox may be used interchangeably with PSO since they are both iterative population-based solvers. See >> help pso (from the ./psopt directory) for more details. Features * NEW: support for distributed computing using MATLAB's parallel computing toolbox. * Full support for bounded, linear, and nonlinear constraints. *

多维函数搜索：（1)最速下降法　(2)阻尼牛顿法 (3)共轭梯度法　(4)鲍维尔法 (5)变尺度法　　(6)单纯形法

人工蜂群算法是模仿蜜蜂行为提出的一种优化方法，是集群智能思想的一个具体应用，它的主要特点是不需要了解问题的特殊信息，只需要对问题进行优劣的比较，通过各人工蜂个体的局部寻优行为，最终在群体中使全局最优值突现出来，有着较快的收敛速度。为了解决多变量函数优化问题.

这是论文An Unconstrained Optimization Test Functions Collection里的测试函数，用Matlab写好了。

一个约束优化方面非常全面、非常好的国外教材。

用MATLAB实现的无约束优化单纯形替换法的程序

解决不同的非线性优化问题，然后可以通过改进求解有约束优化问题

求解约束优化问题的粒子群算法研究.zip 求解约束优化问题的粒子群算法研究.zip

压缩包中分为两部分，一部分为全局优化问题，另一部分为matlab源代码，每个代码块都有大量的注释，很简明，用户也可用本代码求解其他无约束优化问题。