Description: Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.-Swarm intelligence algorithms are based on naturalbehaviors. Particle swarm optimization (PSO) is astochastic search and optimization tool. Changes in thePSO parameters, namely the inertia weight and thecognitive and social acceleration constants, affect theperformance of the search process. This paper presents anovel method to dynamically change the values of theseparameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing thevalues of the PSO parameters. Platform: |
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Author:sky |
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Description: 非常好的优化算法的书，详细介绍了蚁群算法和粒子群算法以及相关的matlab工具箱，讲了理论和应用给出了工具箱的下载地址。
Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. -Optimization algorithm very good book, detailing the ant colony algorithm and particle swarm optimization and related matlab toolbox, talked about the theory and application are given toolbox download address. Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. Platform: |
Size: 5972992 |
Author:dh |
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Description: ：群智能是一种仿生自然界动物昆虫觅食筑巢行为的新兴演化计算技术。目前主要的群智能优化算法有蚁群算法、微粒群算
法和人工鱼群算法。本文介绍了群智能算法的产生、发展和优点，并着力阐述了上述三种典型算法的基本原理，同时概述了各
算法的应用现状，最后提出了算法将来有待研究的内容。-: Swarm Intelligence is a kind of bionic nature nesting animals foraging behavior of insects emerging computing technology evolution. At present, the main group of intelligent optimization algorithms are ant colony algorithm, particle swarm optimization and artificial fish-swarm algorithm. This paper introduces the intelligent algorithm groups the emergence, development and benefits, and focus on the three basic principles of a typical algorithm, the algorithm also provides an overview of the application of the status quo, the end of the algorithm to be the future contents of the study. Platform: |
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Author:duweijie |
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Description: 北京大学计算智能研究室几位计算智能教授的辛苦结晶，包含粒子群算法源代码，PPT文档说明，十五个典型的测试函数，算法注释详尽，简明扼要，是难得的粒子群算法学习材料-Institute of Computational Intelligence Computational Intelligence Research Laboratory Professor of several hard crystal that contains the source code for Particle Swarm Optimization, PPT documentation, 15 typical test functions, the algorithm notes detailed and concise, is a rare particle swarm optimization learning materials Platform: |
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Author:liutianya |
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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: 压缩文件内是一本有关群体智能算法的中文专著，文中详细介绍了粒子群优化算法的原理，并用案例对其进行了详细讲解。-The compressed file is a monograph on swarm intelligence algorithms Chinese paper describes in detail the principle of the particle swarm optimization algorithm, and a case be explained in detail. Platform: |
Size: 1839104 |
Author:ocean |
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Description: 讨论四种群体智能优化算法———蚁群算法、微粒群算法、人工鱼群算法和混合蛙跳算法 ,对其算法的
原理、发展及应用进行了综述。提出了群体智能优化算法统一框架模式 ,并对群体智能优化算法进一步发展进行
了讨论。
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Discuss four swarm intelligence optimization algorithm--- ant colony algorithm, particle swarm optimization, artificial fish swarm algorithm and shuffled frog leaping algorithm, their algorithm theory, development and application are reviewed. Swarm intelligence optimization algorithm proposed unified framework model, and the further development of swarm intelligence optimization algorithms are discussed.
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Author:chenchen |
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Description: 人工智能的作业，是关于粒子群算法的，已经实现完成。可以参考-Artificial intelligence jobs, it s about particle swarm optimization algorithm, has been completed. You can reference Platform: |
Size: 2408448 |
Author:谢佳桦 |
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Description: 粒子群算法的智能优化，PSO 算法属于进化算法的一种，和遗传算法相似，它也是从随机解出发，通过迭代寻找最优解-Particle swarm intelligence optimization, PSO algorithm is an evolutionary algorithm, and genetic algorithm similarity, it is also starting from a random solution, find the optimal solution by iteration Platform: |
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Author:王艳琴 |
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Description: 联合稀疏恢复的Swarm算法，在基本particle swarm optimization (PSO)算法基础上进行改进-Inspired by particle swarm
optimization (PSO) algorithm and some sparse recovery algorithms,
a novel swarm intelligence algorithm called M-SISR is
proposed to solve the problem. In M-SISR, the initial positions of
the swarm are designed using the-thresholding
algorithm, and the update strategy is designed using the ideas of
PSO and some sparse recovery algorithms. Theoretical analysis
shows the good property of the update strategy, and numerical
simulations on random Gaussian data illustrate the efficiency of
M-SISR. Platform: |
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Author:bigbigtom |
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Description: Abstract
This paper present heuristics based study of multi area power network. Heuristic procedures involving Particle Swarm
Intelligence and Fuzzy based inferences have been employed to effectively obtain the optimized gains of PID controller. Any
change in the load demand causes generator’s shaft speed lower than the pre-set value and the system frequency deviates
the standard value results in malfunctioning of frequency relays. A five area load frequency model is constructed in -Abstract
This paper present heuristics based study of multi area power network. Heuristic procedures involving Particle Swarm
Intelligence and Fuzzy based inferences have been employed to effectively obtain the optimized gains of PID controller. Any
change in the load demand causes generator’s shaft speed lower than the pre-set value and the system frequency deviates
the standard value results in malfunctioning of frequency relays. A five area load frequency model is constructed in Platform: |
Size: 870400 |
Author:Gomaa Haroun |
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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: |
Size: 5953536 |
Author:dreamer |
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Search - particle swarm intelligence