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Keywords
(3)
Evolutionary Computing
Genetic Algorithm
particle swarm optimizer
Related Publications
(23)
Particle swarm optimization versus genetic algorithms for phased array synthesis
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Comparison between Genetic Algorithms and Particle Swarm Optimization
Comparison between Genetic Algorithms and Particle Swarm Optimization,10.1007/BFb0040812,Russell C. Eberhart,Yuhui Shi
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Comparison between Genetic Algorithms and Particle Swarm Optimization
(
Citations: 394
)
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Russell C. Eberhart
,
Yuhui Shi
This paper compares two evolutionary computation paradigms: genetic algorithms and
particle swarm
optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.
Conference:
Evolutionary Programming
, pp. 611616, 1998
DOI:
10.1007/BFb0040812
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The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
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)
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www.informatik.unitrier.de
)
Citation Context
(248)
...The PSO technique can generate a highquality solution with short calculation time and a more stable convergence characteristic compared to other evolutionary methods (Eberhart and Shi
1998
, Yoshida
et al...
M. J. Mahmoodabadi
,
et al.
A new optimization algorithm based on a combination of particle swarm ...
...gif"/> are acceleration constants (Eberhart and Shi
1998
);
w
is the inertia weight (Eberhart and Shi
2000
)...
Kit Yan Chan
,
et al.
Handling uncertainties in modelling manufacturing processes with hybri...
...However, PSO searches for an optimum through each particle flying in the search space and adjusting its flying trajectory according to its personal best experience and its neighborhood’s best experience rather than through particles undergoing genetic operations like selection, crossover, and mutation [
5
]...
...The salient feature of PSO lies in its learning mechanism that distinguishes the algorithm from other EC techniques [
5
]...
...For example, given a 3dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2,
5
, 2], its personal best position is Pi = [0, 2, 5] and its neighborhood’s best position is Pn = [5, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
...For example, given a 3dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2, 5, 2], its personal best position is Pi = [0, 2,
5
] and its neighborhood’s best position is Pn = [5, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
...For example, given a 3dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2, 5, 2], its personal best position is Pi = [0, 2, 5] and its neighborhood’s best position is Pn = [
5
, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
...Given the guidance of Po, the updated velocity become Vi = Po − Xi = [0, 0, 1] − [2,
5
, 2] = [−2, −5, −1]; thus the new position is Xi = Xi + Vi = [0, 0, 1], resulting in a new and better position with a cost f(Xi) = 1 that makes the particle fly faster toward the global optimum [0, 0, 0]...
ZhiHui Zhan
,
et al.
Orthogonal Learning Particle Swarm Optimization
...SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family [
4
]...
Jun Zhang
,
et al.
Evolutionary Computation Meets Machine Learning: A Survey
...The best previous position of an individual is recorded so far from the previous generation and is represented as ; the position of the best individual among all the individuals is represented as ; returns a uniform random number in the range of ; is an inertia weight factor; and are acceleration constants [
16
]...
Kit Yan Chan
,
et al.
Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Opti...
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,
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Riccardo Poli
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The particle swarm: social adaptation of knowledge
(
Citations: 399
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James Kennedy
Conference:
International Conference on Evolutionary Computation
, 1997
Sort by:
Citations
(394)
A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for singleobjective and multiobjective problems
M. J. Mahmoodabadi
,
A. Bagheri
,
N. Narimanzadeh
,
A. Jamali
Journal:
Engineering Optimization  ENG OPTIMIZ
, vol. aheadofp, no. aheadofp, pp. 120, 2012
Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence
Kit Yan Chan
,
Tharam S. Dillon
,
C. K. Kwong
Journal:
International Journal of Production Research  INT J PROD RES
, vol. 50, no. 6, pp. 17141725, 2012
Optimal Allocation and Sizing of Active Power Line Conditioners Using a New Particle Swarm Optimizationbased Approach
Iman Ziari
,
Alireza Jalilian
Journal:
Electric Power Components and Systems  ELECTR POWER COMPON SYST
, vol. 40, no. 3, pp. 273291, 2012
Orthogonal Learning Particle Swarm Optimization
(
Citations: 6
)
ZhiHui Zhan
,
Jun Zhang
,
Yun Li
,
YuHui Shi
Journal:
IEEE Transactions on Evolutionary Computation  TEC
, vol. 15, no. 6, pp. 832847, 2011
System identification and control using adaptive particle swarm optimization
(
Citations: 2
)
Alireza Alfi
,
Hamidreza Modares
Journal:
Applied Mathematical Modelling  APPL MATH MODEL
, vol. 35, no. 3, pp. 12101221, 2011