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Multi-swarm optimization

About: Multi-swarm optimization is a(n) research topic. Over the lifetime, 19162 publication(s) have been published within this topic receiving 549725 citation(s).

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Papers
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Proceedings ArticleDOI: 10.1109/ICNN.1995.488968
06 Aug 2002-
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

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Topics: Multi-swarm optimization (77%), Metaheuristic (69%), Stochastic diffusion search (67%) ...read more

32,237 Citations


Journal ArticleDOI: 10.1007/S11721-007-0002-0
James Kennedy, Russell C. Eberhart1Institutions (1)
01 Jan 1995-Swarm Intelligence
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

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Topics: Multi-swarm optimization (77%), Metaheuristic (69%), Particle swarm optimization (65%) ...read more

17,792 Citations


Proceedings ArticleDOI: 10.1109/MHS.1995.494215
Russell C. Eberhart1, James KennedyInstitutions (1)
04 Oct 1995-
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

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Topics: Multi-swarm optimization (69%), Metaheuristic (63%), Particle swarm optimization (62%) ...read more

13,173 Citations


Proceedings ArticleDOI: 10.1109/ICEC.1998.699146
Yuhui Shi1, Russell C. Eberhart1Institutions (1)
04 May 1998-
Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

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8,672 Citations


Journal ArticleDOI: 10.1109/4235.985692
M. Clerc1, James Kennedy2Institutions (2)
Abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

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Topics: Multi-swarm optimization (65%), Particle swarm optimization (65%), Swarm behaviour (59%) ...read more

7,683 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20215
20207
201926
2018171
20171,050
20161,466

Top Attributes

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Topic's top 5 most impactful authors

Andries P. Engelbrecht

91 papers, 7K citations

Ajith Abraham

55 papers, 3K citations

Carlos A. Coello Coello

54 papers, 8.2K citations

Jianchao Zeng

53 papers, 1K citations

Jun Sun

39 papers, 1.6K citations

Network Information
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