### Citations

3886 |
Adaptation in Natural and Artificial Systems. The
- Holland
- 1975
(Show Context)
Citation Context ...is not attained Figure 2: The Pseudo code of the PSO procedure • Mutation provides for occasional disturbances in the crossover operation by inverting one or more genetic elements during reproduction =-=[21, 22, 23]-=-. The pseudo code of the standard GAs is as shown in Figure 3 [24, 23]: Begin GA g=0 generation counter Initialize population Evaluate population P(g) i.e., compute fitness values While not done do g=... |

3769 | Particle Swarm Optimization
- Kennedy, Eberhart
- 1942
(Show Context)
Citation Context ...based stochastic optimization technique for continuous nonlinear functions [1]. PSO was developed in 1995 by Dr. James Kennedy, a social psychologist, and Dr. Russell Eberhart, an electrical engineer =-=[2]-=-. PSO term refers to a relatively new family of algorithms that may be used to find optimal (or near optimal) solutions to numerical and qualitative problems. It is easily implemented in most programm... |

2843 |
Genetic algorithms in search, optimization, and machine learning,”
- Goldberg
- 1989
(Show Context)
Citation Context ...provides for occasional disturbances in the crossover operation by inverting one or more genetic elements during reproduction [21, 22, 23]. The pseudo code of the standard GAs is as shown in Figure 3 =-=[24, 23]-=-: Begin GA g=0 generation counter Initialize population Evaluate population P(g) i.e., compute fitness values While not done do g=g+1 Select P(g) from P(g-1) Crossover P(g) Mutate P(g) Evaluate P(g) E... |

1090 |
An analysis of the behavior of a class of genetic adaptive systems,
- Jong
- 1975
(Show Context)
Citation Context ...is not attained Figure 2: The Pseudo code of the PSO procedure • Mutation provides for occasional disturbances in the crossover operation by inverting one or more genetic elements during reproduction =-=[21, 22, 23]-=-. The pseudo code of the standard GAs is as shown in Figure 3 [24, 23]: Begin GA g=0 generation counter Initialize population Evaluate population P(g) i.e., compute fitness values While not done do g=... |

347 |
The Design of Innovation: Lessons from and for Competent Genetic Algorithms,
- Goldberg
- 2002
(Show Context)
Citation Context ...is not attained Figure 2: The Pseudo code of the PSO procedure • Mutation provides for occasional disturbances in the crossover operation by inverting one or more genetic elements during reproduction =-=[21, 22, 23]-=-. The pseudo code of the standard GAs is as shown in Figure 3 [24, 23]: Begin GA g=0 generation counter Initialize population Evaluate population P(g) i.e., compute fitness values While not done do g=... |

164 |
Digital Image Processing Using Matlab.
- Gonzalez, Woods, et al.
- 2004
(Show Context)
Citation Context ...orhood of each pixel in the given image. In the traditional enhancement technique, the original equation shown in ”(1),” is applied to each pixel at location (i, j) using the following transformation =-=[13]-=-: g(i, j) =[ M ][f(i, j) − m(i, j)] (1) σ(i, j) ISBN:978-988-98671-5-7 WCE 2007Proceedings of the World Congress on Engineering 2007 Vol I WCE The2007, mean July and 2 - 4, standard 2007, London, dev... |

35 | Edges: Saliency measures and automatic thresholding.
- Rosin
- 1999
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Citation Context ...etector that is applied to the transformed image I(Z), n edgels is the number of edgel pixels as detected with the Sobel edge detector. The Sobel detector used here is an automatic threshold detector =-=[15]-=-. M and N are the number of pixels in the horizontal and vertical direction of the image. E(I) is the sum of intensities of the edges included in the enhanced image [16]. Lastly, H(I(z)) measures the ... |

21 |
On the convergence analysis and parameter selection in particle swarm optimization”,
- Zheng, Ma, et al.
- 2012
(Show Context)
Citation Context ...chieved, the particle stores the location of that value as pbest (particle best). The location of the best fitness value achieved by any particle during any iteration is stored as gbest (global best) =-=[18, 19, 20]-=-. Using pbest ISBN:978-988-98671-5-7 WCE 2007Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K. Number of occurrences (frequency) 1500 1000 500 0 0 5... |

14 |
Empirical study of particle swarm optimizer with an increasing inertia weight
- Zheng, Ma, et al.
(Show Context)
Citation Context ...chieved, the particle stores the location of that value as pbest (particle best). The location of the best fitness value achieved by any particle during any iteration is stored as gbest (global best) =-=[18, 19, 20]-=-. Using pbest ISBN:978-988-98671-5-7 WCE 2007Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K. Number of occurrences (frequency) 1500 1000 500 0 0 5... |

8 |
Reliability growth modeling for software fault detection using particle swarm optimization
- Sheta
- 2006
(Show Context)
Citation Context ...he use of PSO to solve variety of problems in computer science and engineering [4, ?, 5]. The use of PSO to solve various problems in pattern recognition and image processing was presented in [6]. In =-=[8]-=-, author used PSO to estimate model parameters for software fault detection and diagnosis. Online training algorithm of a Generalized Neuron (GN) was developed using PSO in [9]. Particle swarm optimiz... |

6 | Towards Automatic Image Enhancement Using Genetic Algorithms, LaSEEB-ISR-Instituto Superior Tcnico
- Munteanu, Rosa
- 2001
(Show Context)
Citation Context ...he representation of the solution and 2. The fitness function. The proposed enhancement model is derived from ”(1), ” and is applied to each pixel at location (i, j) usingthe following transformation =-=[14]-=-: g(i, j) =[K M σ(i, j)+b ][f(i, j)−c∗m(i, j)]+m(i, j)a (2) a, b, c, andk are the parameters defined over the real positive numbers and they are the same for the whole image. Comparing ”(1),” to ”(2),... |

5 |
Enhancement of Chest Radiographs with Gradient Operators
- DaPonte, Fox
- 1988
(Show Context)
Citation Context ...n automatic threshold detector [15]. M and N are the number of pixels in the horizontal and vertical direction of the image. E(I) is the sum of intensities of the edges included in the enhanced image =-=[16]-=-. Lastly, H(I(z)) measures the entropy of the image I(z). The proposed PSO objective is to find the solution that maximizes F (Z). To achieve these objectives we need to: 1. Increase the relative numb... |

3 | Dynamic clustering using support vector learning with particle swarm optimization - Lin, Cheng |

3 |
Particle Swarm Optimization for Image Noise Cancellation
- Chen, Wang, et al.
- 2006
(Show Context)
Citation Context ...plored the use of PSO to solve variety of problems in computer science and engineering [4, ?, 5]. The use of PSO to solve various problems in pattern recognition and image processing was presented in =-=[6]-=-. In [8], author used PSO to estimate model parameters for software fault detection and diagnosis. Online training algorithm of a Generalized Neuron (GN) was developed using PSO in [9]. Particle swarm... |

3 | Venayagamoorthy, “Online Training of a Generalized Neuron with Particle Swarm Optimization
- Kiran, Jetti, et al.
(Show Context)
Citation Context ...as presented in [6]. In [8], author used PSO to estimate model parameters for software fault detection and diagnosis. Online training algorithm of a Generalized Neuron (GN) was developed using PSO in =-=[9]-=-. Particle swarm optimization for image registration was introduced in [10]. This is why it was quite challenging to adjust or tune the PSO parameters such that the required goals are achieved. An emp... |

2 | Simulation-based optimization for repairable systems using particle swarm algorithm - Talal, Mohamed - 2005 |

2 | Pso based gabor wavelet feature extraction method - Sun, Pan, et al. - 2004 |

1 |
Particle swarm optimization for image registration
- Talbi, Batouche
- 2004
(Show Context)
Citation Context ...for software fault detection and diagnosis. Online training algorithm of a Generalized Neuron (GN) was developed using PSO in [9]. Particle swarm optimization for image registration was introduced in =-=[10]-=-. This is why it was quite challenging to adjust or tune the PSO parameters such that the required goals are achieved. An empirical study on the setting of control coefficients in PSO was presented in... |

1 |
When is a swarm necessary?,” pp
- Richer, Blackwell
- 2006
(Show Context)
Citation Context ...arameters such that the required goals are achieved. An empirical study on the setting of control coefficients in PSO was presented in [11]. When should we use swarm to solve problems was explored in =-=[12]-=-. In this paper, a real-coded PSO is applied to adapt the gray-level intensity transformation in the image. The fitness of each image is taken as a swarm particle and its subjective score is given by ... |

1 |
Adaptive particle swarm optimization,” pp
- Yasuda, Ide, et al.
- 2003
(Show Context)
Citation Context ...chieved, the particle stores the location of that value as pbest (particle best). The location of the best fitness value achieved by any particle during any iteration is stored as gbest (global best) =-=[18, 19, 20]-=-. Using pbest ISBN:978-988-98671-5-7 WCE 2007Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K. Number of occurrences (frequency) 1500 1000 500 0 0 5... |

1 |
Improvement of genetic algorithm using PSO and euclidean data distance algorithm
- Dong
(Show Context)
Citation Context ... random solutions and search for the optimum by updating generations. Both have fitness values to evaluate the population. However, the information sharing mechanism in PSO is significantly different =-=[1, 20, 25]-=-. • In GAs, each possible solution within the population of a biological individual is coded in so called chromosome. Chromosomes share information with each other. Each chromosome is assigned a fitne... |