搜索资源列表
yinlisousuosuanfa
- 对新的智能算法：引力搜索算法的介绍 由伊朗科学家于2009年发明-New intelligent algorithm: gravitational search algorithm presented by Iranian scientists invented in 2009
PSOGSA
- 程序提出了一种基于人群混合算法，是粒子群优化和引力搜索算法的组合。主要目标提高整合PSO和GSA算法的能力-Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization
PSOGSA
- 引力搜索算法与粒子群算法结合的混合算法，多大量的标准测试函数进行测试-Hybrid algorithm gravitational search algorithm and particle swarm algorithm combining multiple large number of standard test functions for testing
1
- 基于引力搜索算法的matlab程序，文件提供了源代码和测试函数-the matlab demo based on gravitional search algorithm, the file has source demo and benchmark.
GSA
- 引力搜索算法源代码，matlab实现，简单易懂，适合初学者。-Gravitational search algorithm function optimization
2016.10.04GSA_SVM
- 以优化SVM算法的参数c和g为例，对GSA（引力搜索算法）MATLAB源码进行了详细中文注解。是很好的学习材料。- In order to optimize SVM algorithm parameters c and g as an example, the GSA (Gravitational Search Algorithm) MATLAB source code for a detailed Chinese annotation. Is a good learning materia
GSA
- 引力搜索算法的的matlab原始代码，一种新型的优化算法(GSA algorithm.A new optimization algorithm)
势场法-改进
- 通过这一算法可以实现二维平面内无人机的路径规划。在该平面内存在已知的障碍，势场法通过给终点添加引力，对障碍增加斥力，使得无人机能够顺利的避障并到达终点。通过对传统势场法的改进，避免了无人机容易陷入极值的问题，使搜索更易实现。(Through this algorithm, the path planning of the UAV in the two-dimensional plane can be realized. There are known obstacles in the plane
a56f4835e7c057ad942cb04110437b71
- 引力搜索算法 主要用于非线性系统参数辨识(Gravitational search optimization algorithm for parameter estimation of nonlinear systems)
引力搜索算法（GSA）
- 用matlab实现引力搜索的算法，人工神经网络导航的基本算法(Based on matlab to achieve the gravity search algorithm.)
GGSA
- 改进的万有引力搜索算法的matlab程序，经过测试，100%可以使用的。(gravitational search algorithm matlab program, after testing, 100% can be used.)
PSO和GSA
- 基于群里智能的粒子群优化算法和引力搜索算法有效结合，性能优于原始算法
guijigenzong
- 自动驾驶车辆轨迹跟踪程序，可以运行，可以出图(Gravitational search algorithm program that can run)
2016.10.04GSA_SVM
- 通过引力搜索算法，进行支持向量机参数优化。实现模式识别和趋势预测。(The parameters of support vector machine are optimized by gravitational search algorithm. Realize pattern recognition and trend prediction.)