Description: 用JAVA语言编写，包括PSO（Particle swarm optimization, 中文译名为粒子群优化或微粒群算法), DE (Differential evolution, 中文译名为差分进化或差异演化)等算法，有一些不带约束和带约束的算例（如Michelawicz的几个问题）。使用说明见usage.txt、RUNExample.bat和程序中的注释。
-with Java language, including the PSO (Particle swarm optimization, the Chinese translation of PSO or PSO algorithm), DE (Differential evolution, the Chinese translation of the difference or differential evolution evolution) algorithm, a number of non-binding and binding with the examples (such as the several issues Michelawicz ). Use see usage.txt, RUNExample.bat and procedures of the Notes. Platform: |
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Author:刘三 |
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Description: C语言开发的微粒群优化算法源程序，程序用到了多个测试函数：包括Rosenbrock 函数等，操作时首先打开文件选择一个函数，然后点击计算就可以了。-C language development Particle Swarm Optimization algorithm source, a number of procedures used test functions : Functions include Rosenbrock, the first operation of a choice open file function, Then click on the calculation. Platform: |
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Author:zhangtao |
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Description: 粒子群优化算法（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R．
Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。源于对鸟群捕食的行为研究。粒子群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解．
-Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution. Platform: |
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Author:周荷 |
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Description: A hybrid Particle Swarm Optimization algorithm for functions in the real space. -Particle Swarm Optimization algo rithm for functions in the real space. Platform: |
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Author: |
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Description: 粒子群优化（PSO）算法研究进展综述，本人简要的从八个方面归纳了PSO的研究情况，涉及60余篇文献（主要是IEEE文献），对大多文献进行了简要评价。从文献资料显示，此方面较为系统的归纳尚未见报导，而国内相关成果亦较少。希望通过与大家的共享，以互相交流、共同进步-Particle Swarm Optimization (PSO) algorithm Progress Review, I briefly summarized from eight aspects of the PSO. involving more than 60 papers published (mainly IEEE documents), most of the literature on the summary evaluation. Information from the literature in this area is summarized in the system has not been reported, and there is less relevant results. We hope that through the sharing, exchange, and common progress. Platform: |
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Author:jiangsx |
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Description: 一种新的随机优化技术:基于群落动态分配的粒子群优化算法(Community Dynamic Assignation-based Particle Swarm Optimization,CDAPSO)。新算法通过动态改变粒子群体的组织结构和分配特征来维持寻优过程中启发信息的多样性,从而使其全局收搜索能力得到了显著提高,并且能够有效避免早熟收敛问题。-a new stochastic optimization techniques : Community-based dynamic allocation of PSO algorithm (Dynamic Community Assigna tion-based Particle Swarm Optimization, CDAPSO). New Algorithm for dynamic change particle group's organizational structure and distribution to maintain the optimization process enlightening information diversity, thus the overall admission search capability has been significantly improved, and can effectively prevent premature convergence. Platform: |
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Author:wuyuqian |
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Description: 基于粒子群优化的书籍或程序基于粒子群优化的书籍或程序-Particle Swarm Optimization Based on the books or procedures based on particle swarm optimization books or procedures Platform: |
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Author:王凉 |
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Description: 粒子群算法与遗传算法用于优化的问题求解,可以解决一些-Particle Swarm Optimization and Genetic Algorithms for optimization problem solving, you can solve some Platform: |
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Author:tiger |
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Description: 粒子群算法在离散问题中的应用。这里主要是在TSP问题中的应用。-Discrete Particle Swarm Optimization Problems. Here the main problem in the application of TSP. Platform: |
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Author:李洁 |
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Description: 微粒群优化算法(Particle Swarm Optimization,PSO算法)源于鸟群和鱼群群体运
动行为的研究，是一种新的群体智能优化算法，是演化计算领域中的一个新的分支。它
的主要特点是原理简单、参数少、收敛速度较快，所需领域知识少。该算法的出现引起
了学者们极大的关注，已在函数优化、神经网络训练、组合优化、机器人路径规划等领
域获得了广泛应用，并取得了较好的效果。尽管粒子群优化算法发展近十年，但无论是
理论分析还是实践应用都尚未成熟，有大量的问题值得研究。
-Particle swarm optimization (Particle Swarm Optimization, PSO algorithm) from groups of birds and fish movement behavior, is a new swarm intelligence algorithm, in the field of evolutionary computation is a new branch. Its main feature is simple in principle, few parameters, convergence is faster, less domain knowledge required. The algorithm brought the scholars are of great concern, has been in function optimization, neural network training, combinatorial optimization, robot path planning has been widely used applications, and achieved good results. Despite the development of particle swarm optimization nearly a decade, but both theory and practice applications are not yet mature, a large number of issues worth studying. Platform: |
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Author:赵 |
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Description: 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善，存在一些内在的陷，因而这些应用还不成熟和完善。
(Particle swarm optimization (partieleSwarmOptimization) originated in the birds, fish, and of a
Some simulation of social behavior, is a swarm intelligence-based evolutionary computing. The origin of the niche technology is In genetic algorithms, this method can make random optimization algorithm based on the formation of groups of species, so that the appropriate priority
Algorithm has the ability to find multiple optimal solutions. The integration technology of multiple classifiers is through multiple classifiers into
Some combination of the line to determine the final classification, in order to obtain better than a single classifier performance. Integration of multiple classifiers
Technical requirements for primitive classification is not only better individual performance and the difference to a large degree, which form a niche technology The ability of species has many inherent similarities. The researchers will now have a niche technology used in multisection
Class ens) Platform: |
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Author:dreamer |
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