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Author

James Kennedy

Bio: James Kennedy is a academic researcher from Bureau of Labor Statistics. The author has contributed to research in topic(s): Particle swarm optimization & Multi-swarm optimization. The author has an hindex of 30, co-authored 44 publication(s) receiving 91777 citation(s). Previous affiliations of James Kennedy include United States Department of Labor & University of Indianapolis.

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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


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


Open access
01 Jan 2010-

6,567 Citations


Cited by
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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.

...read more

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

...read more

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

17,792 Citations


Open accessBook
Jorge Nocedal1, Stephen J. Wright2Institutions (2)
01 Nov 2008-
Abstract: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

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Topics: Continuous optimization (54%)

17,225 Citations


Open accessJournal ArticleDOI: 10.1002/JCC.21334
Oleg Trott1, Arthur J. Olson1Institutions (1)
Abstract: AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

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Topics: AutoDock (76%), Protein–ligand docking (55%), Lead Finder (52%) ...read more

14,346 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


Performance
Metrics

Author's H-index: 30

No. of papers from the Author in previous years
YearPapers
20191
20181
20171
20161
20151
20101

Top Attributes

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Author's top 5 most impactful journals

Swarm Intelligence

2 papers, 17.7K citations

Advances in Complex Systems

1 papers, 17 citations

Journal of Experimental Zoology

1 papers, 5 citations

Adaptive Behavior

1 papers, 34 citations

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