An Improved Particle Swarm Optimization Algorithm

2012 ◽  
Vol 195-196 ◽  
pp. 1060-1065
Author(s):  
Chang Yuan Jiang ◽  
Shu Guang Zhao ◽  
Li Zheng Guo ◽  
Chuan Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Condition ◽  
Benchmark Function ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization

Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Particle Swarm Optimization with Team Spirit Inertia Weight

2011 ◽  
Vol 383-390 ◽  
pp. 5744-5750
Author(s):  
Xi Zhen Wang ◽  
Yan Li ◽  
Gang Hu Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Decline Curves ◽  
Team Spirit ◽  
Global And Local

A PSO Algorithm with Team Spirit Inertia weight (TSWPSO) is presented based on the study of the effect of inertia weight on Standard Particle Swarm Optimization (SPSO). Due to the theory of group in organization psychology, swarm is divided into multiple sub-swarms and search is run in a number of different sub-swarms which are parallel performed. Try to find or modify a curve which is compatible with optimized object within many inertia weight decline curves, in order to balance the global and local explorations ability in particle swarm optimization and to avoid the premature convergence problem effectively. The testes by five classical functions show that, TSWPSO has a better performance in both the convergence rate and the precision.

A Particle Swarm Optimization with Variety Factor

2012 ◽  
Vol 239-240 ◽  
pp. 1291-1297 ◽  
Author(s):  
Hai Sheng Qin ◽  
Deng Yue Wei ◽  
Jun Hui Li ◽  
Lei Zhang ◽  
Yan Qiang Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Property ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Previous Algorithm ◽  
Modified Pso

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

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.

Study of Particle Swarm Inertia Weight Adjustment Method Based on Fitness Changes after Multi-Step Iteration

2015 ◽  
Vol 740 ◽  
pp. 696-701
Author(s):  
Shu Hui Zheng ◽  
Ling Yu Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Adjustment Problems ◽  
Swarm Optimization ◽  
Adjustment Method ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Optimization Simulation

Considering the inertia weight adjustment problems in the standard particle swarm optimization algorithm, a kind of particle swarm inertia weight adjustment method based on multi-step iteration fitness changes was put forward, and by analyzing if particle optimal fitness values was further optimized after a certain number of iterations, then how to set the inertia weight was determined, which can balance the particle swarm global optimization and local optimization. Simulation results show that the improved algorithm was better than the standard particle swarm optimization algorithm in convergence speed and accuracy of solution.

scholarly journals A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Michala Jakubcová ◽  
Petr Máca ◽  
Pavel Pech
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Random Permutation ◽  
Swarm Optimization ◽  
Distribution Strategy ◽  
Inertia Weight ◽  
Functional Value ◽  
Significant Difference

We compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO), the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO), and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO). All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.

scholarly journals POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2018 ◽  
Vol 6 (6) ◽  
pp. 335-345
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Swarm Optimization ◽  
Polar Function ◽  
Standard Particle Swarm Optimization ◽  
Power Problem

This paper presents Polar Particle Swarm optimization (PPSO) algorithm for solving optimal reactive power problem. The standard Particle Swarm Optimization (PSO) algorithm is an innovative evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. In the proposed PPSO algorithm that enhances the behaviour of PSO and avoids the local minima problem by using a polar function to search for more points in the search space in order to evaluate the efficiency of proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms. Simulation results demonstrate good performance of the Polar Particle Swarm optimization (PPSO) algorithm in solving an optimal reactive power problem.

ON THE ANALYSIS OF THE PERFORMANCES OF PARTICLE SWARM OPTIMIZATION ALGORITHM WITH GLOBALLY AND LOCALLY TUNED INERTIA WEIGHT VARIANTS

2006 ◽  
Vol 03 (01) ◽  
pp. 97-114 ◽  
Author(s):  
M. SENTHIL ARUMUGAM ◽  
MACHAVARAM VENKATA CHALAPATHY RAO
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Hybrid System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
Inertia Weight

The investigation of the performance of Particle Swarm Optimization (PSO) algorithm with the new variants to inertia weight in computing the optimal control of a single stage hybrid system is presented in this paper. Three new variants for inertia weight are defined and their applicability with the PSO algorithm is thoroughly explained. The results obtained through the new proposed methods are compared with the existing PSO algorithm, which has a time varying inertia weight from a higher value to a lower value. The proposed methods provide both faster convergence and optimal solution with better accuracy.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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