A new and improved version of particle swarm optimization algorithm with global–local best parameters

Arumugam, M. Senthil; Rao, M. V. C.; Chandramohan, Aarthi

doi:10.1007/s10115-007-0109-z

Cited by 68 publications

(16 citation statements)

References 16 publications

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“…Inter-ested reader may refer [1][2][3][4][5] for detailed information. M. Senthil Arumugam et al [6] proposed a new version of PSO (called as GLBestPSO) which approach global best solution in different manner. Further modification of same algorithm is done in 2009 by the same authors [7].…”

Section: Particle Swarm Optimization [Pso]mentioning

confidence: 99%

“…Each algorithm has been designed with certain goals such as minimizing total number of fitness evaluations to reach nearly optimal solution and to capture diverse optimal solutions in multi-modal solutions. Moreover all these algorithms should be able to escape from local optimal solutions which are possible only if random parameters used in these algorithms are properly tuned [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Improved environmental adaption method and its application in test case generation

Mishra

Tiwari

Misra

2014

Journal of Intelligent &Amp; Fuzzy Systems

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An Environmental Adaption Method (EAM) has been recently proposed in which, each solution updates its structure on the basis of current environmental fitness and its own fitness. EAM uses a new operator known as adaption operator. In this paper, an improved EAM, henceforth called as IEAM has been proposed and proper tuning of parameters of algorithm has been done to find the optimal solution in minimum time. IEAM has been tested on a set of benchmark functions. Results show the superiority of IEAM over PSO-TVAC, SADE and other optimization algorithms. Even for complex functions, the performance of IEAM is very good. In addition to this, IEAM has also been applied to software testing to generate test cases for white box testing.

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Section: Particle Swarm Optimization [Pso]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved environmental adaption method and its application in test case generation

Mishra

Tiwari

Misra

2014

Journal of Intelligent &Amp; Fuzzy Systems

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“…Following the work of Angeline, various mutation operators for a single stage hybrid manufacturing system via conventional PSO (cPSO) and global-local best PSO (GLBest PSO) are incorporated [3]. Later, Lovbjerg et al [4]chose to investigate the effect of cross-over operator with PSO.…”

Section: Review Of Cpso and Glbestpso Algorithmsmentioning

confidence: 99%

“…The performance of all the three PSO algorithms is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms. This paper is organised as follows: in Section 2, a brief review of cPSO and GLBest PSO algorithms is presented; in Section 3, the proposed hybrid PSO algorithm is described, discuss the design of the fme tuning elements such as mutation, cross-over; in Sections 4, the performance of the cPSO, GLBest PSO, pf-PSO, ePSO and HPSO algorithms with and without the fme tuning elements are analysed and compared for three difficult benchmark problems; fmally the conclusions are given in Section 5 The TVIW which is developed is given in equation (3).…”

mentioning

confidence: 99%

A fine tuning hybrid particle swarm optimization algorithm

Tang

Zhao

2009

2009 International Conference on Future BioMedical Information Engineering (FBIE)

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Particle swarm optimization (PSO) has shown itsgood performance in many optimization problems. This paper introduces a new approach called hybrid particle swarm optimization like algorithm (HPSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). The performance of all the three PSO algorithms is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms. This paper is organised as follows: in Section 2, a brief review of cPSO and GLBest PSO algorithms is presented; in Section 3, the proposed hybrid PSO algorithm is described, discuss the design of the fme tuning elements such as mutation, cross-over; in Sections 4, the performance of the cPSO, GLBest PSO, pf-PSO, ePSO and HPSO algorithms with and without the fme tuning elements are analysed and compared for three difficult benchmark problems; fmally the conclusions are given in Section 5The TVIW which is developed is given in equation (3).The GLBest PSO method [8] in which, the inertia weight and acceleration coefficients are neither set to a constant value nor set as a linearly decreasing time varying function. Instead, they are defmed as a function of local best (pbest) and global best (gbest) values of the particles in each generation. The II. REVIEW OF cPSO AND GLBESTPSO ALGORITHMSThe cPSO algorithm considered in this paper comprises standard PSO algorithm[5][6] with TVIW and time varying acceleration coefficient (TVAC). The velocity and position equation for this method is given in equations (1) and (2) (3)(2)where WI and w 2 are the initial and fmal values of the inertia weight respectively, iter is the current iteration number and maxiter is the maximum number of allowable iterations. (4) and (5). Ratnaweera et al.[7] introduced a TVAC which is given in equations

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“…Still, performance advantages of these approaches can be achieved only when the noise probability is low. Furthermore, there are many other filters proposed for removing impulse noise based on machine learning techniques [13,15,16,19]. For instance, the histogram based fuzzy filter (HFF) [22], Lee et-al.…”

mentioning

confidence: 99%

Fuzzy based Impulse Noise Reduction Method

Hussain

Bhatti

Jaffar

2011

Multimed Tools Appl

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In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-tonoise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.

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A new and improved version of particle swarm optimization algorithm with global–local best parameters

Cited by 68 publications

References 16 publications

Improved environmental adaption method and its application in test case generation

Improved environmental adaption method and its application in test case generation

A fine tuning hybrid particle swarm optimization algorithm

Fuzzy based Impulse Noise Reduction Method

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