Modified immune particle swarm optimization algorithm and its application

Fu, Duan; Su, Tong-fen

doi:10.3724/sp.j.1087.2010.01883

Cited by 6 publications

(4 citation statements)

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“…If the current optimal position is not the global optimal solution, the particle swarm can't search again in the solution space. Algorithm will be falling into local optimal solution, thus it will appear the so-called premature convergence phenomenon [12,13].…”

Section: Basic Particle Swarm Optimization Algorithm (Pso) and Itmentioning

confidence: 99%

“…Therefore appropriate learning factors can accelerate convergence speed, it can avoid to falling into the local optimal solution as well. Learning factors updating formula can be represented as: (12) There is no doubt that the velocity needs changing when it is too high (such as ), which can avoid the particles flying out of target area. The velocity updating formula is represented as:…”

Section: The Improved Particle Swarm Optimization Algorithm(mlammentioning

confidence: 99%

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Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation

Tan¹,

Li²,

Huang³

2015

Proceedings of the First International Conference on Information Science and Electronic Technology

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Abstract-As a representative method of swarm intelligence, Particle Swarm Optimization (PSO) is an algorithm for searching the global optimum in the complex space through cooperation and competition among the individuals in a population of particle. But the basic PSO has some demerits, such as relapsing into local optimum solution, slowing convergence velocity in the late evolutionary. To solve those problems, an particle swarm optimization with comprehensive learning & self-adaptive mutation(MLAMPSO) was proposed. The improved algorithm made adaptive mutation on population of particles in the iteration process, at the same time, the weight and learning factors were updated adaptively. It could enhance the ability of PSO to jump out of local optimal solution. The experiment results of some classic benchmark functions show that the improved PSO obviously improves the global search ability and can effectively avoid the problem of premature convergence.

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Section: Basic Particle Swarm Optimization Algorithm (Pso) and Itmentioning

confidence: 99%

Section: The Improved Particle Swarm Optimization Algorithm(mlammentioning

confidence: 99%

Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation

Tan¹,

Li²,

Huang³

2015

Proceedings of the First International Conference on Information Science and Electronic Technology

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show abstract

“…In order to make smile image, the lips corners should rise. So, we first generate two triangles with vertices (1, 2, 4) and (2,3,4). Then, to warp the two triangles, some other triangles should be defined outside them.…”

Section: Expression Deformationmentioning

confidence: 99%

An Automatic Deformation for Facial Smile using PSO Detection

2017

JEIS

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Abstract:To improve the convenience of triangulation based image warping algorithm, an automatic approach for facial expression deformation is presented. In order to guarantee the expression deformation is dealt with high quality and conveniently, the approach uses two key technologies: the PSO detection and the affine transformation. The PSO algorithm is utilized to search the optimal position of rectangle containing face and eyes. The affine transformation is applied to warp related triangles to deform expression. The simulation results show that this method can produce natural smile expression, without marking the spots manually, especially works well for some shy smile expressions.

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“…The learning factors c 1 and c 2 are non-negative constants which represent the weight of the particle preferences, c 1 represents the preference of their own experience, c 2 represents the preference of the group experience. According to the experience, c 1 and c 2 are set to 2.05 in practice [12]. The random numbers r 1 and r 2 get the values between (0, 1).…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm

Jin¹,

Zhang²,

Yang³

et al. 2012

Measurement Science Review

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The Fuzzy C-Mean clustering (FCM) algorithm is an effective image segmentation algorithm which combines the clustering of non-supervised and the idea of the blurry aggregate, it is widely applied to image segmentation, but it has many problems, such as great amount of calculation, being sensitive to initial data values and noise in images, and being vulnerable to fall into the shortcoming of local optimization. To conquer the problems of FCM, the algorithm of fuzzy clustering based on Particle Swarm Optimization (PSO) was proposed, this article first uses the PSO algorithm of a powerful global search capability to optimize FCM centers, and then uses this center to partition the images, the speed of the image segmentation was boosted and the segmentation accuracy was improved. The results of the experiments show that the PSO-FCM algorithm can effectively avoid the disadvantage of FCM, boost the speed and get a better image segmentation result.

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Modified immune particle swarm optimization algorithm and its application

Cited by 6 publications

References 0 publications

Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation

Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation

An Automatic Deformation for Facial Smile using PSO Detection

Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm

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