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.

Particle Swarm Optimization Using Various Inertia Factor Variants

2011 ◽  
Vol 460-461 ◽  
pp. 54-59
Author(s):  
Jun Tang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of manufacturing systems. Three different inertia factor, a constant inertia factor (CIF), time-varying inertia factor (TVIF), and global-local best inertia factor (GLbestIF), are considered with the particle swarm optimization(PSO) algorithm to analyze the impact of inertia factor on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia factor separately to compute the optimal control of the manufacturing system, and it is observed that the PSO with the proposed inertia factor yields better resultin terms of both optimal solution and faster convergence. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

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 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.

scholarly journals On the optimal control of single-stage hybrid manufacturing systems via novel and different variants of particle swarm optimization algorithm

2005 ◽  
Vol 2005 (3) ◽  
pp. 257-279 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Manufacturing Systems ◽  
Hypothesis Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight

This paper presents several novel approaches of particle swarm optimization (PSO) algorithm with new particle velocity equations and three variants of inertia weight to solve the optimal control problem of a class of hybrid systems, which are motivated by the structure of manufacturing environments that integrate process and optimal control. In the proposed PSO algorithm, the particle velocities are conceptualized with the local best (orpbest) and global best (orgbest) of the swarm, which makes a quick decision to direct the search towards the optimal (fitness) solution. The inertia weight of the proposed methods is also described as a function of pbest and gbest, which allows the PSO to converge faster with accuracy. A typical numerical example of the optimal control problem is included to analyse the efficacy and validity of the proposed algorithms. Several statistical analyses including hypothesis test are done to compare the validity of the proposed algorithms with the existing PSO technique, which adopts linearly decreasing inertia weight. The results clearly demonstrate that the proposed PSO approaches not only improve the quality but also are more efficient in converging to the optimal value faster.

scholarly journals On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Single Stage ◽  
Hybrid Manufacturing ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Real Coded Genetic Algorithm ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

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.

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm for the Knapsack Problem

2013 ◽  
Vol 373-375 ◽  
pp. 1178-1181
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm Optimization ◽  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Local Optimal Solution

An improved Quantum-behaved particle swarm optimization algorithm is proposed for 0-1 knapsack problem.In the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. The simulation experiments show that improved Quantum-behaved particle swarm optimization algorithm has better convergence and stability in solving knapsack problem.

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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