Description: PSO’s precursor was a simulator of social behavior, that was used to visualize
the movement of a birds’ flock. Several versions of the simulation model
were developed, incorporating concepts such as nearest-neighbor velocity
matching and acceleration by distance
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Size: 8192 |
Author:sina mehrabi |
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Description: Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.-Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space. Platform: |
Size: 3093504 |
Author:Beta |
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Description: 主要是关于一些粒子演算法的思想及如何进行分析。并且粒子演算法的一些衍生算法- PSO is a recently proposed algorithm, motivated
from the simulation of social behavior.
PSO is based on the evolutionary computation
technique.
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Size: 6055936 |
Author:dfa |
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Description: Cockroach Swarm Optimization
a new bionic algorithm, entitled
Cockroach Swarm Optimization (CSO), that is inspired by the
social behavior of cockroaches. We construct some models by
imitating the foraging behaviors of cockroach, and describe the
steps of CSO. Simulation results illustrate that CSO has
stronger convergence performance and highly-accuracy
optimization results compares with Particle Swarm
Optimization (PSO), Chaotic Particle Swarm Optimization
(CPSO) and Artificial Fish-swarm Algorithm (AFSA). Platform: |
Size: 2048 |
Author:sina valizade |
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Description: 受帝国主义殖民竞争机制的启发，Atashpaz-Gargari和Lucas于2007年提出了一种新的智能优化算法—帝国竞争算法 (ICA)。与GA, PSO, ABC等受生物行为启发的群智能算法不同，ICA受社会行为启发，通过摸拟殖民地同化机制和帝国竞争机制而形成的一种优化方法。ICA也是一种基于群体的优化方法，其解空间由称为国家的个体组成。ICA将国家分为几个子群，称为帝国。在每个帝国内，ICA通过同化机制使非最优的国家(殖民地)向最优国家(帝国主义国家)靠近，该过程类似于PSO。帝国竞争机制是ICA的关键，ICA通过帝国竞争机制将最弱帝国中的一个或多个殖民地移动到其他帝国，使帝国之间可以进行信息交互。(Inspired by the imperialist colonial competition mechanism, Atashpaz-Gargari and Lucas proposed a new intelligent optimization algorithm, Empire competition algorithm (ICA), in 2007. With GA, PSO, ABC and other biological behavior of swarm intelligence algorithm inspired by social behavior, ICA heuristic, an optimization method is formed by simulation of colonial assimilation mechanism and competition mechanism of the empire. ICA is also a swarm based optimization approach whose solution space consists of individuals called states. ICA divides the country into several subgroups, called empires. Within each Empire, ICA moves the non optimal country (colony) to the best country (the imperialist state) through the assimilation mechanism, which is similar to PSO. Imperial competition is the key to ICA, and ICA moves one or more colonies of the weakest Empire to other empires through imperial competition, allowing the Empire to interact with each other.) Platform: |
Size: 16384 |
Author:xzfff
|
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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 - Simulation of Social Behavior