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Optimization of drilling path using the bees algorithm by Kamaruddin, Shafie, Rosdi, Mohamad Naqiuddin, Sukindar, Nor Aiman

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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Inverse identification of elastic properties of composite materials using hybrid GA-ACO-PSO algorithm by Tam, Jun Hui, Ong, Zhi Chao, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee, Li, Wen L.

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Identification of material properties of composite plates using Fourier-generated frequency response functions by Tam, Jun Hui, Ong, Zhi Chao, Lau, Chun Lek, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee

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Radio Frequency Identification (RFID) Indoor Parking Control System by El-Hageen, Hazem M. M., Mohammad Ata, Karimeh Ibrahim, Che Soh, Azura, Ishak, Asnor Juraiza, Jaafar, Haslina

“…In this study, a parking management system is designed using RFID technology along with wireless RF ZigBee to control and manage parking for mall workers. …”
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Angle Based Protein Tertiary Structure Prediction Using Bees Optimization Algorithm by Al-Qattan, Zakaria Noor Aldeen Mahmood

“…In this project, angles based control with Bees Optimization search algorithm were adopted to search with guidance the protein conformational space in order to find the optimum solution. …”
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Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lacta... by Mei, Kie Hon, Mohd Saberi, Mohamad, Abdul Hakim, Mohamed Salleh, Yee, Wen Choon, Kauthar, Mohd Daud, Muhammad Akmal, Remli, Mohd Arfian, Ismail, Omatu, Sigeru, Sinnott, Richard O., Corchado, Juan Manuel

“…The objective function of the hybrid algorithm is optimized using a swarm intelligence optimization algorithm and a Simple Constrained Artificial Bee Colony (SCABC) algorithm. …”
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SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

“…The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter by ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati

“…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

“…TCP factors and a weighted fitness function are also used for test case optimisation. The contributions of this study straddle research perspectives of enhancing Regression Testing with Particle Swarm-Artificial Bee Colony Algorithm, and practical perspectives by providing software testing practitioners the TCP framework that can facilitate and accelerate the production of high-quality software products by revealing faults early and reducing time, cost, and human efforts through automation.…”
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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter by Yazid, E., Liew, M.S., Parman, S., Kurian, V.J.

“…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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Optimization of supply chain management by simulation based RFID with XBEE Network by Soomro, Aftab Ahmed

“…Among the widely used technology in recent years is Radio Frequency Identification (RFID). …”
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System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach by Abdullah Hashim, Azrul Azim, Abdul Ghani, Nor Maniha, Ahmad, Salmiah, Nasir, Ahmad Nor Kasruddin

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System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach by Azrul Azim, Abdullah Hashim, Nor Maniha, Abdul Ghani, Salmiah, Ahmad, Ahmad Nor Kasruddin, Nasir

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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”