Location:
Search - pso benchmark
Search list
Description: 最新SD期刊上关于改进型PSO算法，我是通过学校内部数据库在SD下载的哦！包括《A dynamic inertia weight particle swarm optimization algorithm》、《Adaptive Particle Swarm Optimization》、《Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies》。这三篇都是比较有研究价值的学术文章，识货的请下载！-Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algo-rithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 diff erent
dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search perfor-mance on the benchmark functions signifi cantly.
Platform: |
Size: 2744320 |
Author: asdwe |
Hits:
Description: This code expains kalmanswarm optimization method.All files have been written on matlab 2007a. This method has been explianed with various benchmark functions. This optimization method can be directly compared with other unconstrained optimization method like GA or pso for effieciency and speed.
This method is much faster as compared to genetic algorithim.
Platform: |
Size: 5120 |
Author: missed2010 |
Hits:
Description: This code expains bayesian particle swarm optimization method.All files have been written on matlab 2007a. This method has been explianed with various benchmark functions. This optimization method can be directly compared with other unconstrained optimization method like GA or pso for effieciency and speed.
Platform: |
Size: 4096 |
Author: missed2010 |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer.
Platform: |
Size: 6144 |
Author: ezuezaimie |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer.
Platform: |
Size: 7168 |
Author: ezuezaimie |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer.
Platform: |
Size: 4096 |
Author: ezuezaimie |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer.
Platform: |
Size: 6144 |
Author: ezuezaimie |
Hits:
Description: benchmark pso test system
Platform: |
Size: 2048 |
Author: javad |
Hits:
Description: pso algoritm for a benchmark function. it works very good
Platform: |
Size: 1024 |
Author: panaah |
Hits:
Description: 老外写的PSO算法，含基准测试函数，内容丰富-PSO algorithm written by foreigners, including the benchmark function, rich in content
Platform: |
Size: 14336 |
Author: 江山 |
Hits:
Description: In this project, the standard particles swarm optimization - named here (MyPSO) is simulated and tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). Then, a new mechanism- named here (Improved MyPSO) is introduced to improve the performance of the standard particles swarm optimization to achieve better results and avoid stagnation at bad results. This new mechanism is also tested by the same benchmark functions. The results of improved PSO are then compared with those of standard PSO and different published improved PSO (SPSO, PSO-XD, CPSO-S and PSO-P5).
Platform: |
Size: 3072 |
Author: basem |
Hits:
Description: In this project, the standard particles swarm optimization - named here (MyPSO) is simulated and tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). Then, a new mechanism- named here (Improved MyPSO) is introduced to improve the performance of the standard particles swarm optimization to achieve better results and avoid stagnation at bad results. This new mechanism is also tested by the same benchmark functions. The results of improved PSO are then compared with those of standard PSO and different published improved PSO (SPSO, PSO-XD, CPSO-S and PSO-P5).
Platform: |
Size: 20083712 |
Author: basem |
Hits:
Description: PSO基准函数,非常好的。请下载，对大家很有帮助。-PSO benchmark functions, very good.
Platform: |
Size: 6259712 |
Author: 为实 |
Hits:
Description: 该MATLAB代码用于实现PSO-w方法对benchmark函数的优化-This matlab code is designed for PSO-w algorithm
Platform: |
Size: 3072 |
Author: 杨大哥 |
Hits:
Description: 该MATLAB代码用于实现对PSO-cf算法的实现，用于对Benchmark函数的优化-This matlab code is designed for PSO-cf algorithm
Platform: |
Size: 3072 |
Author: 杨大哥 |
Hits:
Description: 标准PSO算法用于实现对Benchmark函数的优化-This matlab code is designed for the standard PSO algorithm
Platform: |
Size: 4096 |
Author: 杨大哥 |
Hits:
Description: 该代码为benchmark测试函数验证标准PSO算法的代码，经过测试，该代码没有问题。-The code for the benchmark test function verification standard PSO algorithm code has been tested, the code is no problem.
Platform: |
Size: 3072 |
Author: 李勇 |
Hits:
Description: 在pso粒子群优化函数中的典型测试基准函数，应用检测pso算法的优劣性(In the PSO particle swarm optimization function of the typical test benchmark function, the application of PSO algorithm to check the pros and cons)
Platform: |
Size: 13312 |
Author: 1286705494
|
Hits:
Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithms
Platform: |
Size: 78848 |
Author: DirtyMind |
Hits:
Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithms.
Platform: |
Size: 12879872 |
Author: DirtyMind |
Hits: