Optimal position and setting of svc using heuristic optimization techniques

2017 ◽  
Author(s):  
Rahul Agrawal ◽  
S.K. Bharadwaj ◽  
D. P. Kothari
Keyword(s):  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Optimal Position

scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

scholarly journals Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

2020 ◽  
Author(s):  
P. Ravichandran ◽  
Meenakshipriya B ◽  
R. Parameshwaran ◽  
C. Maheswari ◽  
E.B. Priyanka ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Depth Of Penetration ◽  
Swarm Optimization ◽  
Aisi 430

Abstract The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.

scholarly journals A BBO/PSO based Hybrid Technique for Distribution System Feeder Reconfiguration

2019 ◽  
Vol 9 (2) ◽  
pp. 1156-1159
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Cooperative Strategy ◽  
Optimal Position ◽  
Radial Distribution System ◽  
Optimum Position ◽  
New Feature

A cooperative strategy to reconfigure the feeder network by maximizing the location and volume of the distribution generator (DG) in the power system was addressed in this report. The new feature of the proposed method is the integrated output of the Biography Based Optimization (BBO) and PSO techniques. The above methods are the optimization techniques used to configure the radial distribution system for the optimal position and capacities of the DG. For determining the optimum position and strength of the DG, the BBO algorithm includes radial distribution network voltage, actual and reactive energy. The input parameters of BBO are classified into sub settings here and are allowed as the optimization of the PSO algorithm. The PSO synthesizes the problem and uses sub-parameters to create the sub-solution. The method of BBO migration and mutation is used to determine the optimal position and ability of DG for the sub solution of PSO. The cooperative strategy introduced is then applied on the system MATLAB / Simulink, and the usefulness is evaluated using BBO and PSO techniques. The findings of the analysis demonstrate the strength of the solution suggested and affirm its capacity for resolving the problem.

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