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- 18 април 2005
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Се работи за интелегенцијата на јатото, на колониите на организми кои работат инстинктивно за заедничко добро.
Од набљудувањето на однесувањето на инсектите, птиците, рибите, бактериите, се раѓаат бројни алгоритми. Темата е денес многу актуелна и полека ќе треба да се оди во таа насока.
Комунизам конечно како треба да биде
,
Системот е децентрализиран, секој во него си ја знае улогата.
Сигурно сте приметиле птици например како сите заедно се движат, јата риби како вртат насока, организираноста на пчелите, матица, работнички итн. Има три интересни книги кои мене ми се омилени од Бернард Вербер, може да го ѕирнете линкот за однесувањето меѓу мравките, тие се СФ книги но даваат некоја интересна претстава.
Swarm Intelligence (SI) is an Artificial Intelligence technique involving the study of collective behaviour in decentralized systems. Such systems are made up by a population of simple agents interacting locally with one other and with their environment. Although there is typically no centralized control dictating the behaviour of the agents, local interactions among the agents often cause a global pattern to emerge. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, honey bees, bacteria, and many more. Swarm-like algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have already been applied successfully to solve real-world optimization problems in engineering and telecommunication. SI models have many features in common with Evolutionary Algorithms. Like EA, SI models are population-based. The system is initialized with a population of individuals (i.e., potential solutions). These individuals are then manipulated over many iteration steps by mimicking the social behaviour of insects or animals, in an effort to find the optima in the problem space. Unlike EAs, SI models do not explicitly use evolutionary operators such as crossover and mutation. A potential solution simply 'flies' through the search space by modifying itself according to its past experience and its relationship with other individuals in the population and the environment.
Има неколку области
Particle Swarm Optimization
Differential Evolution
Ant Colony Optimization
Culture algorithms
Other nature-inspired computation techniques
Multi-objective Optimization
Constrained Optimization
Optimization in Dynamic Environments
Swarm Intelligence models for evolving Neural Networks
Scheduling
Game Learning
Comparative studies of Swarm Intelligence models
Real world applications
Ако го интересира некој околу темава, тука сум.
Од набљудувањето на однесувањето на инсектите, птиците, рибите, бактериите, се раѓаат бројни алгоритми. Темата е денес многу актуелна и полека ќе треба да се оди во таа насока.
Комунизам конечно како треба да биде
,Системот е децентрализиран, секој во него си ја знае улогата.
Сигурно сте приметиле птици например како сите заедно се движат, јата риби како вртат насока, организираноста на пчелите, матица, работнички итн. Има три интересни книги кои мене ми се омилени од Бернард Вербер, може да го ѕирнете линкот за однесувањето меѓу мравките, тие се СФ книги но даваат некоја интересна претстава.
Swarm Intelligence (SI) is an Artificial Intelligence technique involving the study of collective behaviour in decentralized systems. Such systems are made up by a population of simple agents interacting locally with one other and with their environment. Although there is typically no centralized control dictating the behaviour of the agents, local interactions among the agents often cause a global pattern to emerge. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, honey bees, bacteria, and many more. Swarm-like algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have already been applied successfully to solve real-world optimization problems in engineering and telecommunication. SI models have many features in common with Evolutionary Algorithms. Like EA, SI models are population-based. The system is initialized with a population of individuals (i.e., potential solutions). These individuals are then manipulated over many iteration steps by mimicking the social behaviour of insects or animals, in an effort to find the optima in the problem space. Unlike EAs, SI models do not explicitly use evolutionary operators such as crossover and mutation. A potential solution simply 'flies' through the search space by modifying itself according to its past experience and its relationship with other individuals in the population and the environment.
Има неколку области
Particle Swarm Optimization
Differential Evolution
Ant Colony Optimization
Culture algorithms
Other nature-inspired computation techniques
Multi-objective Optimization
Constrained Optimization
Optimization in Dynamic Environments
Swarm Intelligence models for evolving Neural Networks
Scheduling
Game Learning
Comparative studies of Swarm Intelligence models
Real world applications
Ако го интересира некој околу темава, тука сум.