Search Results - (( model evaluation case algorithm ) OR ( parameter optimization swarm algorithm ))

An Improved Hybrid of Particle Swarm Optimization and the Gravitational Search Algorithm to Produce a Kinetic Parameter Estimation of Aspartate Biochemical Pathways by Ahmad Muhaimin, Ismail, Mohd Saberi, Mohamad, Hairudin, Abdul Majid, Khairul Hamimah, Abas, Safaai, Deris, Zaki, Nazar, Siti Zaiton, Mohd Hashim, Zuwairie, Ibrahim, Muhammad Akmal, Remli

“…The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. …”
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An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system by Islam N.N., Hannan M.A., Shareef H., Mohamed A.

“…Damping; Eigenvalues and eigenfunctions; Electric power systems; Learning algorithms; Optimization; Particle swarm optimization (PSO); Problem solving; State space methods; Test facilities; Backtracking search algorithms; Multi machine power system; Power system damping; Power system oscillations; Power system stability; Power System Stabilizer; System stability; algorithm; Article; backtracking search algorithm; bacterial foraging optimization algorithm; machine; mathematical analysis; mathematical computing; mathematical parameters; particle swarm optimization; power supply; power system stabilizer; process optimization; statistical model…”

Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm by Aljanad A., Tan N.M.L., Agelidis V.G., Shareef H.

Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics by Mohammad Jarrah, Mu'ath Ibrahim

“…Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset.…”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…Seven performance indexes were examined to evaluate the performance of the proposed Muskingum model integrated with IBA, with other models that were also based on the Muskingum Model with three-parameters but utilized different optimization algorithms. …”
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Development of a two-level trade credit model with shortage for deteriorating products using hybrid metaheuristic algorithm by Molamohamadi, Zohreh

“…A hybrid metaheuristic algorithm which combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, is then developed to solve the established models. …”
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Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks by Abdolrasol M.G.M., Hannan M.A., Suhail Hussain S.M., Ustun T.S., Sarker M.R., Ker P.J.

Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model by Mohammed Adam, Kunna Azrag

“…In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. …”
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OPTIMIZATION OF PID CONTROLLER PARAMETERS USING ARTIFICIAL FISH SWARM ALGORITHM by SOOMRO, WAFA ALI SOOMRO

“…This Final Year Project is preceded on the topic named “The Optimization of PID Control Parameters Using Artificial Fish Swarm Algorithm”. …”
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Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation by Premkumar M., Ravichandran S., Hashim T.J.T., Sin T.C., Abbassi R.

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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System by Nasroddin, Saidatul Nizan, Mohd Mokji, Musa, Tan, Kok, Zainal Abidin, Amar Faiz, Amirulah, Rahman, Nordin, Nur Anis, Hasim, Saipol Hadi, Zakaria, Hamzah, Hassan, Jefery, Jaafar, Hazriq Izzuan, Khairuddin, Osman

“…This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. …”
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

“…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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Effect of parameters variation on the performance of particle swarm optimization algorithm for tag coverage problem of radio frequency identification network by Nawawi, Azli, Hasnan, Khalid, Ngali, Zamani, Sidek, Noor Azizah

“…In this paper, PSO (Particle Swarm Optimization) algorithm is used to optimize the tag coverage problem. …”
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Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram M., Othman F.B., Yaseen Z.M., Afan H.A., Allawi M.F., Malek M.B.A., Ahmed A.N., Shahid S., Singh V.P., El-Shafie A.

“…Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States…”

Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach by Nur'atiah Zaini

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

“…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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Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank System by Ismail, Mohd Khairuddin, Amira Sarayati, Ahmad Dahalan, Amar Faiz, Zainal Abidin, Yee, Yang Lai, Nur Anis, Nordin, Siti Fatimah, Sulaiman, Hazriq Izzuan, Jaafar, Syahrul Hisham, Mohamad, Noor Hafizah, Amer

“…These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). …”
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Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning Of PID Controller in Industrial Couple Tank System by Mohd Khairuddin, Ismail, Ahmad Dahalan, Amira Sarayati, Zainal Abidin, Amar Faiz, Yee, Yang Lai, Nordin, Nur Anis, Siti Fatimah, Sulaiman, Jaafar, Hazriq Izzuan, Mohamad, Syahrul Hisham, Amer, Noor Hafizah

“…These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). …”
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Normative Fish Swarm Algorithm For Global Optimization With Applications by Tan, Weng Hooi

“…Referred to as Normative Fish Swarm Algorithm (NFSA), the proposed Fish Swarm Algorithm, Optimized by Particle Swarm Optimization with Extended Memory (PSOEM-FSA) is expanded by amalgamating the normative knowledge to provide supplementary guidelines for better global optimum achievement and convergence rate. …”
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