Search Results - (( loading optimisation based algorithm ) OR ( using optimization method algorithm ))

Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network by Bujal, Noor Ropidah

“…Finally, an AHP was integrated with FA to form Firefly Analytical Hierarchy Algorithm (FAHA) to automatically calculate the weight of each objective function based on the load flow outputs followed by the optimisation process. …”
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Modelling of optimal placement and sizing of battery energy storage system using hybrid whale optimization algorithm and artificial immune system for total system losses reduct... by Wong Ling Ai

“…Lastly, the effectiveness of WOA and WOA-AIS in attaining optimal solutions was validated with other well-known optimisation algorithms, including particle swarm optimisation (PSO) and firefly algorithm (FA). …”

Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation by Zhanuzak, Raiymbek, Ala'anzy, Mohammed Alaa, Othman, Mohamed, Algarni, Abdulmohsen

“…Furthermore, the EDLB algorithm enhances load balancing by 46.46%. These results highlight the effectiveness of the EDLB algorithm in addressing critical load balancing issues and surpassing existing methods. …”
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Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah by Tuan Ahmad Fauzi, Tuan Abdullah

“…The main reasons of using these optimization methods is to propose a minimum total cost and lowest voltage drop of electrical system design in buildings.Comparison between the optimisation methods and without using optimisation methods show that the total cost and total voltage drop are lower when using optimisation methods. …”
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Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system by Ali, Hazem I.

“…The particle swarm optimization (PSO) method is used to tune the parameters of the controller and weighting functions subject to QFT and/or constraints. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

Optimal short term load forecasting using LSSVM and improved BFOA considering Malaysia pandemic disrupted situation by Zaini, Farah Anishah

“…The IBFOA is proposed by modifying the chemotaxis process in BFOA using a Sine Cosine Algorithm (SCA), which improves the convergence speed and accuracy of the algorithm. …”
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A decomposition/aggregation method for solving electrical power dispatch problems by Mansor M.H., Irving M.R., Taylor G.A.

“…Subsequently, the solutions from the areas are combined (aggregated) to solve the overall system problem. The results obtained using the decomposition / aggregation method are compared with the results found when the ED Problem was solved using a centralised EP approach and the base-case results found from solving a (non-optimal) load flow. …”

Particle Swarm Optimised Controller for Solid-State Transfer Switch Towards Fast Power Transfer and PQ Mitigation by Sebastian G., Hannan M.A., Al-Shetwi A.Q., Ker P.J., Muttaqi K.M., Uddin M.N.

A new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping considering uncertainties by Norhafidzah, Mohd Saad, Muhamad Zahim, Sujod, Mohd Ikhwan, Muhammad Ridzuan, Mohammad Fadhil, Abas, Mohd Shawal, Jadin, Mohd Fadzil, Abdul Kadir

“…The Monte Carlo-embedded MVMO-SH was then used to optimise PVDG in the urban RDN. Simulations were run for several scenarios in three load cases based on 288 segments: residential, commercial, and industrial urban loads. …”
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Particle Swarm Optimization Algorithm Based Fuzzy Controller for Solid-State Transfer Switch Toward Fast Power Transfer and Power Quality Mitigation by Hannan M.A., Sebastian G., Al-Shetwi A.Q., Ker P.J., Rahman S.A., Mansor M., Muttaqi K.M., Uddin M.

“…The objective of this research is to propose an innovative method for improving the efficiency and productivity of fuzzy logic controllers (FLCs) under the influence of harmonics in non-linear loads, by using particle swarm optimization (PSO) for solid-state transfer switch (SSTS). …”

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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Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach by Othman, Anwar Hasan Abdullah, Kassim, Salina, Rosman, Romzie, Redzuan, Nur Harena

“…The optimal model employs a multilayer neural network (NN) along with an “optimised operator” with the ability to locate the optimal factor loading of the applied algorithm. …”
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Immunized-evolutionary algorithm based technique for loss control in transmission system with multi-load increment by Shaaya S.A., Musirin I., Sulaiman S.I., Mansor M.H.

“…This paper presents immunized-evolutionary algorithm based technique for loss control in transmission system with multi -load increment. …”

Malaysia residential load profile management based on time of use tariff using ant colony optimization algorithm by Dahlan, Nofri Yenita, Sulaima, Mohamad Fani, Wan Hanapi, Wan Noor Azzyati, Noor Din, Muhamad Muhtazam

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Optimising islanded AC microgrid control: A hierarchical approach with FCS-VMPC and consensus algorithm by Khan, Zawar, Muhammad Aamir, Muhammad Aamir, Khan, Rizwanullah, Mohd Yousof, Mohd Fairouz

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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…To achieve these goals, a cell selection algorithm is proposed that overcomes the issues of conflicts and load imbalance while reducing energy consumption. …”
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Novel techniques for voltage stability assessment and improvement in power system / Ismail Musirin. by Musirin, Ismail

“…A new Evolutionary programming (EP) based acceleration search algorithm was developed to accelerate the process in estimating maximum load ability in a power system. …”
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Priority-based vehicle-to-grid scheduling for minimization of power grid load variance by Hashim M.S., Yong J.Y., Ramachandaramurthy V.K., Tan K.M., Mansor M., Tariq M.