An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 394 ◽  
pp. 505-508 ◽  
Author(s):  
Guan Yu Zhang ◽  
Xiao Ming Wang ◽  
Rui Guo ◽  
Guo Qiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Genetic Mutation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Optimum Path ◽  
Improved Pso

This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

An Improved Particle Swarm Optimization Algorithm

2012 ◽  
Vol 538-541 ◽  
pp. 2658-2661
Author(s):  
Ri Su Na ◽  
Qiang Li ◽  
Li Ji Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Negative Gradient ◽  
Improved Pso

Based on the standard particle swarm optimization an improved PSO algorithm was introduced in this paper. The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergence is improved by joining the negative gradient. By adding negative term on standard PSO formula, combining with coefficient of negative gradient term and inertia weight , lead to effectively balance between the local and global search ability. It will accelerate convergence and avoid local optimum. Moreover, from the bionic point of view, this improved PSO algorithm is closer to the reality of the actual situation of the bird flocking. From the simulation results of four typical test functions, it can be seen that an improved particle swarm optimization with negative gradient can significantly improve the solving speed and solution quality.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

Design and Simulation of an Improved Particle Swarm Optimization

2010 ◽  
Vol 20-23 ◽  
pp. 1280-1285
Author(s):  
Jian Xiang Wei ◽  
Yue Hong Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Dimensional Space ◽  
Numerical Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Diversity ◽  
Test Problems ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Pso

The particle swarm optimization (PSO) algorithm is a new population search strategy, which has exhibited good performance through well-known numerical test problems. However, it is easy to trap into local optimum because the population diversity becomes worse during the evolution. In order to overcome the shortcoming of the PSO, this paper proposes an improved PSO based on the symmetry distribution of the particle space position. From the research of particle movement in high dimensional space, we can see: the more symmetric of the particle distribution, the bigger probability can the algorithm be during converging to the global optimization solution. A novel population diversity function is put forward and an adjustment algorithm is put into the basic PSO. The steps of the proposed algorithm are given in detail. With two typical benchmark functions, the experimental results show the improved PSO has better convergence precision than the basic PSO.

A Particle Swarm Optimization Algorithm Based on the Pattern Search Method

2012 ◽  
Vol 532-533 ◽  
pp. 1664-1669 ◽  
Author(s):  
Jun Li Zhang ◽  
Da Wei Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Pattern Search ◽  
Local Optimum ◽  
Effective Mechanism ◽  
Swarm Optimization

For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm， personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.

CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 3-7 ◽  
Author(s):  
Nan Ping Liu ◽  
Fei Zheng ◽  
Ke Wen Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Multiuser Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Discrete Pso ◽  
Case Simulation ◽  
Multi Access

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

scholarly journals An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shouwen Chen ◽  
Zhuoming Xu ◽  
Yan Tang ◽  
Shun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization ◽  
Inertia Weight ◽  
Ipso Algorithm

Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm with BPSO, EPSO, CPSO, and ACL-PSO algorithms, experimental results show that the proposed IPSO algorithm is more efficient; it also outperforms other four baseline PSO algorithms in accuracy.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 850-851 ◽  
pp. 809-812
Author(s):  
Hong Mei Ni ◽  
Wei Gang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Swarm Behavior ◽  
Peak Function ◽  
Improved Pso

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.

scholarly journals Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dawei Gao ◽  
Xiangyang Li ◽  
Haifeng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Extremum ◽  
Local Optimum ◽  
Global Optimality ◽  
Critical Position ◽  
Swarm Optimization ◽  
Improved Pso

In the optimization design process, particle swarm optimization (PSO) is limited by its slow convergence, low precision, and tendency to easily fall into the local extremum. These limitations make degradation inevitable in the evolution process and cause failure of finding the global optimum results. In this paper, based on chaos idea, the PSO algorithm is improved by adaptively adjusting parameters r1 and r2. The improved PSO is verified by four standard mathematical test functions. The results prove that the improved algorithm exhibits excellent convergence speed, global search ability, and stability in the optimization process, which jumps out of the local optimum and achieves global optimality due to the randomness, regularity, and ergodicity of chaotic thought. At last, the improved PSO algorithm is applied to vehicle crash research and is used to carry out the multiobjective optimization based on an approximate model. Compared with the results before the improvement, the improved PSO algorithm is remarkable in the collision index, which includes vehicle acceleration, critical position intrusion, and vehicle mass. In summary, the improved PSO algorithm has excellent optimization effects on vehicle collision.

Heat Transfer Enhancement Across a Pair of Confined Cylinders Using Improved Particle Swarm Optimization Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi ◽  
Ehsan Mirzakhalili
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Pso Algorithm ◽  
Transformation Method ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The dissipation of the heat generated by electronic devices is the key issue in design and development of such products. The trend, especially in the computer industries, has been reducing the size and increasing the computing power of the electronic elements. Studies have indicated that the thermal performance of a micro-channel depends on its geometric parameters and flow conditions. Many techniques have been developed to enhance the performance of confined elliptical cylinders while minimizing the momentum loss. In this paper, a new robust optimization technique is presented. This new technique is an improved Particle Swarm Optimization (PSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests. The case study in this paper is a two dimensional incompressible flow of non-Newtonian power-law fluid over a pair of elliptical tandem cylinders confined in a channel. A new curve parameterization named Class-Shape-Refinement-Transformation method is used to modify the shape of the confined cylinders, and its control points are adopted as the design variables. Furthermore, final solutions obtained from the Territorial Particle Swarm Optimization algorithm reveal an evident improvement over the test case cylinder across all objective functions presented.

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