Search Results - (( java implication based algorithm ) OR ( _ simulation ((models algorithm) OR (bees algorithm)) ))

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway by Ahmad Muhaimin, Ismail, Muhammad Akmal, Remli, Yee Wen, Choon, Nurul Athirah, Nasarudin, Norsyahidatul Nazirah, Ismail, Mohd Arfian, Ismail, Mohd Saberi, Mohamad

“…The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. …”
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Autonomous object movement in modeling bees foraging by Theab, Mustafa Muwafak, Yusof, Yuhanis

“…Experiment conducted using Bees simulation environment shows that the required bees have successfully moved to the identified location.…”
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Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System by Wang, Chen, Wood, Lincoln Christopher, Li, Heng, Aw, Zhenye, Keshavarzsaleh, Abolfazl

“…This study aims to develop a fire evacuation routing model "Bee-Fire" using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. …”
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System performances analysis of reservoir optimization�simulation model in application of artificial bee colony algorithm by Hossain M.S., El-Shafie A., Mahzabin M.S., Zawawi M.H.

Protein Conformantional Search Using Bees Algorithm by Bahamish, Hesham Awadh A., Abdullah, Rosni, Salam, Rosalina Abdul

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Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network by Muhammad Sidik, Siti Syatirah

“…Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. …”
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System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm by Hossain, Md Shabbir, El-Shafie, Ahmed, Mahzabin, Mst Sadia, Zawawi, Mohd Hafiz

“…Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are also used in a view of comparing model performances. …”
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A quick gbest guided artificial bee colony algorithm for stock market prices prediction by Shah, Habib, Tairan, Nasser, Garg, Harish, Ghazali, Rozaida

“…The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. …”
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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators by Nor Maniha, Abdul Ghani, Nizaruddin, M. Nasir, Azrul Azim, Abdullah Hashim

“…This study aims to evaluate the effectiveness of two optimization algorithms, artificial bee colony (ABC) and spiral dynamic algorithm (SDA), in controlling the position of a flexible-link manipulator. …”
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Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

“…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. Overall from the simulation results, it is realized that the proposed CS based NN algorithms performs better than all other proposed and conventional models in terms of CPU Time, MSE, SD and accuracy.…”
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Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network by Husna, Jamal Abdul Nasir

“…Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. …”
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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms by Almubaidin M.A., Ahmed A.N., Sidek L.M., AL-Assifeh K.A.H., El-Shafie A.

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Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing by Parmesivan, Yuganes, Hasan, Sazlinah, Muhammed, Abdullah

“…Cloud computing has become biggest buzz in the computer era these days.It runs entire operating systems on the cloud and doeverything on cloud to store data off-site.Cloud computing is primarily based on grid computing, but it’s a new computational model.Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services.The primary substance of cloud computing is to deal the computing power,storage,different sort of stages and services which assigned tothe external users on demand through the internet.Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing.In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example,when cloud clients try to access and send request tothe same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers.Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded.A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time.The proposed hybrid algorithm has been experimented by using CloudSim simulator.The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm.…”
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Grey Wolf Optimizer for Solving Economic Dispatch Problems by Wong, Lo Ing, M. H., Sulaiman, Mohd Rusllim, Mohamed, Hong, Mee Song

“…The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). …”
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LSSVM parameters tuning with enhanced artificial bee colony by Mustaffa, Zuriani, Yusof, Yuhanis

“…To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the optimized hyper-parameters of LSSVM algorithm.In this paper, an Enhanced Artificial Bee Colony (eABC) is used to obtain the ideal value of LSSVM’s hyper parameters, which are regularization parameter, γ and kernel parameter, σ2.Later, LSSVM is used as the prediction model. …”
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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

“…This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. 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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Structural optimization of 4-DOF agricultural robot arm by Nurul Emylia Natasya Ahmad Zakey, Mohd Hairi Mohd Zaman, Mohd Faisal Ibrahim

“…The DH convention is commonly used to solve kinematic analysis problems of a robot arm. Simulation of kinematic modeling is performed using MATLAB software. …”
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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.

“…This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. 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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Application of intelligent optimization techniques and investigating the effect of reservoir size in calibrating the reservoir operating policy by Hossain Md.S., El-Shafie A., Mohtar W.H.M.W.

“…Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are used to compare the model performances. …”

Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches by Mohd Pauzi, Nur Fazlinda

“…In this simulation, onlooker bees as the important bee make decision to choose the specific method. …”
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