An improved particle swarm optimization based on difference equation analysis

Yang, Qiaohe; Tian, Jinpeng; Si, Wen

doi:10.1080/10236198.2016.1199691

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…PSO learned from this scenario and used it to solve optimization problems. PSO in this paper are using the improvement PSO based on [12]. Figure 1 shows the pseudocode that will be used in this paper.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Thus, in this paper will use Particle Swarm Optimization (PSO) as an optimization technique to optimize the values of shape parameters of beta-spline and the sum squared distance of beta-spline curve. The PSO technique used in this study is the improvement of PSO which refer to [12]. After the values of shape parameters are obtained, cubic beta-spline is used to fit the data of 2D font image of ‫ى‬ and δ fonts.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Shape Parameters of Beta-Spline Using Particle Swarm Optimization

Suliman

Afrouzi²

2018

IJET

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Beta-spline is an alternative curve for 2D font representation. It is preferred since it has G2 continuity and two shape parameters, that can be used to control the curve shape. These shape parameters can also be used to optimize the error between fitted curve and original data points. Commonly, most of the researcher use value of shape parameters of beta-spline as and or some of the researcher choose any random value of these two shape parameters that suitable to be used in beta-spline curve fitting. The values of shape parameters are very important since the values affect the total error of the fitted curves. Thus, in this paper, Particle Swarm Optimization (PSO) is employed to determine the optimum value of the two shape parameters that will optimize the approximation error of the fitted curve. The technique is applied on two fonts: ى and δ, and tested using various number of iterations and populations.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Shape Parameters of Beta-Spline Using Particle Swarm Optimization

Suliman

Afrouzi²

2018

IJET

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“…In the CPSO-IWA, the swarm consists of particles, each of which based on components such that the velocity of the related component can be defined as given in Equations (12) and (13) for the th component of th particle [19]. The CPSO-IWA initialization parameters are given in Table 3.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Performance of PSO Based Classical and Intelligent Controllers for Water Level Control of a Steam Generator

Altaş¹,

Ozkop²

2017

deufmd

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Abstract:In this paper, different controller techniques based on particle swarm optimization (PSO) algorithm are proposed to control the water level of a steam generator with multiple inputmultiple output (MIMO) characteristics. The techniques employed are classical proportional+integral+derivative (PID) control, fuzzy logic control (FLC) and fuzzy tuned proportional-integral control (FTPIC). Gains of PID controller and parameters of FLC (the core and the boundaries of triangular membership functions in input and output spaces) are optimized by the PSO. Validations of the proposed PSO based PID control (PSO-PID), PSO based fuzzy logic control (PSO-FLC) and PSO based fuzzy tuned PI control (PSO-FTPIC) techniques are done with numerical simulation in using MATLAB. The simulation results show that the PSO-PID provides better performance for controlling the water level of a steam generator compared to the others. Buhar Generatörünün Su Seviyesi Denetimi için PSO Temelli Klasik veAkıllı Denetleyicilerin Performansı Keywords Denetim, Bulanık Mantık, Parçacık Sürü, Optimizasyon, ÇGÇÇ Özet: Bu çalışmada çok giriş-çok çıkış (ÇGÇÇ) özelliğine sahip buhar generatörünün su seviyesi denetimi için parçacık sürü optimizasyonu (PSO) algoritmasına dayanan farklı kontrol teknikleri önerilmektedir. Bu teknikler, klasik oransal-integraltürevsel (PID) denetim, bulanık mantık denetim (BMD) ve bulanık ayarlı oransal-integral denetimdir. PID denetleyicilerin kazançları ve BMD'nin parametreleri (giriş ve çıkıştaki üçgen üyelik fonksiyonların merkezleri ve sınırları) PSO tarafından en uygun hale getirilmektedir. Önerilen PSO temelli PID denetim (PSO-PID), PSO temelli bulanık mantık denetim (PSO-BMD) ve PSO temelli bulanık ayarlı PI denetim (PSO-BAPI) tekniklerinin gerçeklemesi, MATLAB kullanılarak sayısal benzetim ile doğrulanmaktadır. Benzetim sonuçları, buhar generatörünün su seviyesi denetim için PSO-PID tekniğini diğerlerine göre daha iyi performans sergilediğini göstermektedir.

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“…Additionally, particles change their velocities and next positions by regarding previous recorded gbest. Note that gbest is recorded for every step that the best value is expected to be found at the end of the iteration [22].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A bat-inspired algorithm for prioritizing test cases

Öztürk

2017

Vietnam J Comput Sci

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By ordering test cases, early fault detection is focused on test case prioritization. In this field, it is widely known that algorithm and coverage criteria focused works are common. Previous works, which are related to test case prioritization, showed that practitioners need a novel method that optimizes test cases according to the cost of each test case instead of regarding the total cost of a test suite. In this work, by utilizing local and global search properties of a bat algorithm, a new bat-inspired test cases prioritization algorithm (BITCP) is proposed. In order to develop BITCP, test case execution time and the number of faults were adapted to the distance from the prey and loudness, respectively. The proposed method is then compared with four methods which are commonly used in this field. According to the results of the experiment, BITCP is superior to the conventional methods. In addition, as the complexity of the code of test cases increases, the decline in average percentage of fault detection is less in BITCP than the other four comparison algorithms produced.

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An improved particle swarm optimization based on difference equation analysis

Cited by 19 publications

References 14 publications

Optimizing the Shape Parameters of Beta-Spline Using Particle Swarm Optimization

Optimizing the Shape Parameters of Beta-Spline Using Particle Swarm Optimization

Performance of PSO Based Classical and Intelligent Controllers for Water Level Control of a Steam Generator

A bat-inspired algorithm for prioritizing test cases

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