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  1. 1
    Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm

    Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm by Al Hazza, Muataz, Adesta, Erry Yulian Triblas, Superianto, M. Y., Riza, Muhammad

    Published 2012
    “…Machining of hard materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the cutting temperatures and surface roughness becomes utmost important to avoid any damage to the quality surface.This paper presents the development of new models and optimizing these models of machining parameters to minimize the cutting temperature in end milling process by integrating the genetic algorithm (GA) with the statistical approach. …”
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  2. 2
    Optimization Of 3d Reconstruction Surface Rendering Algorithm For Osferion Bone Void Filling

    Optimization Of 3d Reconstruction Surface Rendering Algorithm For Osferion Bone Void Filling by Chin, Daniel Jie Yuan

    Published 2023
    “…Thus, the objectives are to enhance the Marching Cubes or the Marching Tetrahedra algorithm for large CT/MRI datasets so that the reconstructed 3D models are rendering-device-agnostic and optimized and to improve the quality of the 3D models after reducing the number of vertices and faces so that the surface of the 3D models can be improved. …”
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  3. 3
    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization

    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization by Ahmad N., Janahiraman T.V.

    Published 2023
    Conference Paper
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  4. 4
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  5. 5
    Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis

    Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis by Asrul, Adam

    Published 2018
    “…In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. …”
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  6. 6
    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization

    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization by Nooraziah Ahmad, Tiagrajah V. Janahiraman

    “…While, particle swarm optimization give the lowest surface roughness when compared to Taguchi method and genetic algorithm and it can optimize faster than genetic algorithm.…”
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  7. 7
    Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms

    Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms by Fauzi, Nur Faiqah, Mohamad Jaya, Abdul Syukor, Mohammad Jarrah, Mu’ath Ibrahim, Akbar, Habibullah

    Published 2017
    “…In order to represent the process variables and coating roughness, a quadratic polynomial model equation was developed. Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. …”
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  8. 8
    Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm

    Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm by Golshan, Abolfazl

    Published 2013
    “…Response surface methodology was utilized to develop mathematical models of the process outputs. …”
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  9. 9
    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm

    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm by Alam, Md. Shah, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah, Konneh, Mohamed

    Published 2010
    “…The developed quadratic prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness.…”
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  10. 10
    Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches

    Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches by Ong, Pauline, Vui, Desmond Daniel Sheng Chin, Choon, Sin Ho, Chuan, Huat Ng

    Published 2018
    “…The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. …”
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  11. 11
    Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation

    Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation by Hamimu, La

    Published 2011
    “…Therefore the use of genetic algorithm (GA) optimization technique which is one of nonlinear optimization methods is an appropriate choice to solve surface wave inversion problem having high nonlinearity and multimodality. …”
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  12. 12
    Improving earth surface temperature forecasting through the optimization of deep learning hyper-parameters using barnacles mating optimizer

    Improving earth surface temperature forecasting through the optimization of deep learning hyper-parameters using barnacles mating optimizer by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Muhammad 'Arif, Mohamad

    Published 2024
    “…To improve the DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) is integrated to optimize both weights and biases. …”
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  13. 13
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coated WC-co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Karim, A.N. Mustafizul, Ginta, Turnad Lenggo, Patwari, Muhammed Anayet Ullah

    Published 2007
    “…Then the developed RS model is further interfaced with the GA to optimize the cutting conditions for achieving minimum surface roughness. …”
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  14. 14
    Modeling Of Curves And Surfaces Using Ght-Bernstein Basis Functions And Using Optimization Methods To Construct Developable Surfaces

    Modeling Of Curves And Surfaces Using Ght-Bernstein Basis Functions And Using Optimization Methods To Construct Developable Surfaces by Bibi, Samia

    Published 2024
    “…The developability degree of the surface is the objective function in optimization techniques. …”
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  15. 15
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coated WC-Co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  16. 16
    Analysis and evaluation of various aspects of solar radiation in the Palestinian territories

    Analysis and evaluation of various aspects of solar radiation in the Palestinian territories by Ismail M.S., Moghavvemi M., Mahlia T.M.I.

    Published 2023
    “…Calculations of the optimized tilt and surface azimuth angles on monthly, seasonal and yearly basis were conducted, with the genetic algorithm being used for this purpose. …”
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  17. 17
    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization

    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

    Published 2022
    “…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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  18. 18
    Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology

    Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

    Published 2015
    “…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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  19. 19
    Surface roughness optimization in end milling using the multi objective genetic algorithm approach

    Surface roughness optimization in end milling using the multi objective genetic algorithm approach by Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Riza, Muhammad, Mohammad Yuhan, Suprianto

    Published 2012
    “…This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. …”
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  20. 20
    A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

    A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process by Shin, Teh Muy, Asrul, Adam, Amar Faiz, Zainal Abidin

    Published 2019
    “…In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. …”
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