惩罚函数法在模拟退火算法求解非线性约束优化问题中的应用，陈思源，，本文首先介绍了模拟退火算法和惩罚函数法的基本原理和方法，然后将其结合成求解非线性约束优化问题的算法。在Matlab语言环境下编制

针对常规遗传算法会出现早熟现象、局部寻优能力较差等不足，在遗传算法运行中融入模拟退火算法算子，实现了模拟退火的良好局部搜索能力与遗传算法的全局搜索能力的结合。经验证，该混合算法可以显著提高遗传算法的运行效率和优化性能。

Non linear equality and inequality constrained PSO（非线性等式与不等式约束PSO）利用粒子群算法求解非线性等式和不等式约束的最小值。包括matlab完整代码。

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. *