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.

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.

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.

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.

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.

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.

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.

scholarly journals Study on Mechanical Design Optimization Based on Improved Particle Swarm Optimization Algorithm

2015 ◽  
Vol 9 (1) ◽  
pp. 961-965
Author(s):  
Zhou Ning ◽  
Zhang Jing
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm Optimization Algorithm ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Improved Pso

In view of local optimization in particle swarm optimization algorithm (PSO algorithm), chaos theory was introduced to PSO algorithm in this paper. Plenty of populations were generated by using the ergodicity of chaotic motion. The uniformly distributed initial particles of the particle swarms were extracted from the populations according to the Euclidean distance between particles, so that the particles could uniformly distribute in the solution space. Local search was carried out on the optimal position of the particles during evolution, so as to improve the development capability of PSO algorithm and prevent its prematurity, thus enhancing its global optimizing capability. Then the improved PSO algorithm was applied to mechanical design optimization. With optimization design for two-stage gear reducer as the study object, objective function and constraint conditions were determined by building a mathematical model of optimization design, thus realizing optimization design. Simulation and comparison between the improved algorithm and unimproved algorithm show that improved PSO algorithm can optimize the optimization results of PSO algorithm at a faster convergence rate.

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