Search Results - (( new evaluation colony algorithm ) OR ( storage optimization based algorithm ))*

A new multiple ant colonies optimization algorithm utilizing average pheromone evaluation mechanism by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

“…Multiple ant colonies optimization is an extension of the Ant Colony Optimization framework It offers a good opportunity to improve the ant colony optimization algorithms by encouraging the exploration of a wide area of the search space without losing the chance of exploiting the history of the search.This paper proposes a new multiple ant colonies optimization algorithm that is based on ant colony system and utilizes ave rage pheromone evaluation mechanism.The new algorithm divides the ants’ populations into multiple ant colonies and can be used to tackle large volume combinatorial optimization problems effectively. …”
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Optimizing large scale combinatorial problems using multiple ant colonies algorithm based on pheromone evaluation technique by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

“…The new algorithm is based on the ant colony system and utilizes average and maximum pheromone evaluation mechanisms. …”
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Optimization algorithms for energy storage integrated microgrid performance enhancement by Roslan M.F., Hannan M.A., Ker P.J., Muttaqi K.M., Mahlia T.M.I.

Backtracking Search Algorithm Based Fuzzy Charging-Discharging Controller for Battery Storage System in Microgrid Applications by Faisal M., Hannan M.A., Ker P.J., Uddin M.N.

Pumped-storage scheduling using particle swarm optimization / Amirul Asraf Razali by Razali, Amirul Asraf

“…This thesis presents the solution algorithm based on the particle swarm optimization (PSO) for solving the pumped-storage (P/S) scheduling problem. …”
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Revisiting the pheromone evaluation mechanism in the interacted multiple ant colonies optimization framework by Aljanaby A., Ku-Mahamud K.R., Norwawi N.Md.

Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

“…The framework of Interacted Multiple Ant Colonies Optimization (IMACO) is a recent proposition.It divides the ants'population into several colonies and employs certain techniques to organize the work of these colonies.This paper proposes new effective evaluation and exploration techniques for IMACO and experimentallv tests the stagnation behavior of IMACO. …”
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A new ant based rule extraction algorithm for web classification by Ku-Mahamud, Ku Ruhana, Saian, Rizauddin

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Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications by Hannan M.A., Faisal M., Jern Ker P., Begum R.A., Dong Z.Y., Zhang C.

Optimizing Cloud Storage Costs: Introducing the Pre-Evaluation-Based Cost Optimization (PECSCO) Mechanism by Alomari M.F., Mahmoud M.A., Gharaei N., Rasool S.M., Hasan R.A.

“…Nonetheless, cloud service providers impose fees on users based on the volume of data transmitted to and from cloud storage, resulting in elevated storage costs. …”

Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future by Abu S.M., Hannan M.A., Mansor M., Ker P.J., Yaw Long C.

“…The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. …”

Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm by Muthana, Shatha Abdulhadi, Ku Mahamud, Ku Ruhana

“…The gray relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers by Peddakapu, K., Mohd Rusllim, Mohamed, Srinivasarao, P., Arya, Yogendra

“…Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). …”
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Switching Time Optimization via Time Optimal Control for Natural Gas Vehicle Refueling by Mahidzal Dahari, Mahidzal

“…In this thesis, a refueling algorithm using Time Optimal Control (TOC) technique is proposed as a basis for determining the optimal switching time in NGV refueling using the mass and mass flowrate as the state variables, measured using Coriolis flowmeter. …”
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Charging and discharging model of lithium-ion battery for charge equalization control using particle swarm optimization algorithm by Hoque M.M., Hannan M.A., Mohamed A.

Investigating dam reservoir operation optimization using metaheuristic algorithms by Lai V., Essam Y., Huang Y.F., Ahmed A.N., El-Shafie A.

Optimal Algorithms for Energy Storage Systems in Microgrid Applications: An Analytical Evaluation Towards Future Directions by Reza M.S., Rahman N., Wali S.B., Hannan M.A., Ker P.J., Rahman S.A., Muttaqi K.M.

Data Encryption-Enabled Cloud Cost Optimization and Energy Efficiency-Based Border Security Model by Alomari M.F., Mahmoud M.A., Yusoff Y.B., Gharaei N., Abdalla R.A., Gunasekaran S.S.

Grey Wolf Optimizer Based Battery Energy Storage System Sizing for Economic Operation of Microgrid by Sukumar S., Marsadek M., Ramasamy A., Mokhlis H.