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Multi-swarm optimization

Index Multi-swarm optimization

Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. [1]

6 relations: Differential evolution, Estimation of distribution algorithm, Hybrid algorithm, Mendeley, Particle swarm optimization, Swarm intelligence.

Differential evolution

In evolutionary computation, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

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Estimation of distribution algorithm

Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions.

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Hybrid algorithm

A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same problem, either choosing one (depending on the data), or switching between them over the course of the algorithm.

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Mendeley

Mendeley is a desktop and web program produced by Elsevier for managing and sharing research papers, discovering research data and collaborating online.

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Particle swarm optimization

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

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Swarm intelligence

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial.

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References

[1] https://en.wikipedia.org/wiki/Multi-swarm_optimization

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