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  1. 1
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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    Monograph
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  2. 2
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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  3. 3
    Resource management in grid computing using ant colony optimization

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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  4. 4
    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network

    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

    Published 2012
    “…Hence, this indicates that Invasive Weed Optimization could be implemented as a new learning algorithm for an Artificial Neural Network.…”
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  5. 5
    Optimal chiller loading solution for energy conservation using Barnacles Mating Optimizer algorithm

    Optimal chiller loading solution for energy conservation using Barnacles Mating Optimizer algorithm by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2022
    “…This paper proposes an application of evolutionary optimization algorithm, Barnacles Mating Optimizer (BMO) to solve the optimal chiller loading (OCL) problem for minimization of the power consumption in the multi chiller system. …”
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  6. 6
    Optimal design of EV aggregator for real-time peak load shaving and valley filling

    Optimal design of EV aggregator for real-time peak load shaving and valley filling by Solanke T.U., Khatua P.K., Ramachandaramurthy V.K., Yong J.Y., Kanesan J., Tariq M., Kasinathan P.

    Published 2023
    Conference Paper
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  7. 7
    Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia

    Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia by Zaini, Farah Anishah, Sulaima, Mohamad Fani, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

    Published 2024
    “…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. …”
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  8. 8
    Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia

    Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia by Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

    Published 2024
    “…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short‑term daily electricity load in Peninsular Malaysia. …”
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  9. 9
    Grid load balancing using enhance ant colony optimization

    Grid load balancing using enhance ant colony optimization by Ku-Mahamud, Ku Ruhana, Abdul Nasir, Husna Jamal, Mohamed Din, Aniza

    Published 2011
    “…This study presents a new algorithm based on ant colony optimization for load balancing management in grid computing. …”
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  10. 10
    Comparison Between the Bees Algorithm and Genetic Algorithm Model in Manpower Allocation on Cell Loading Problem

    Comparison Between the Bees Algorithm and Genetic Algorithm Model in Manpower Allocation on Cell Loading Problem by AlMahasneh, Hossam Sayel

    Published 2010
    “…Results show that there are different factors of GA that it is not exist in the Bees Algorithm. Both of the proposed algorithm finds optimal or near optimal solutions for the MACL especially in large problems.…”
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  11. 11
    Optimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm

    Optimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm by Nur Iffah, Mohamed Azmi, Nafrizuan, Mat Yahya, Ho, Jun Fu, Wan Azhar, Wan Yusoff

    Published 2019
    “…Proportional-integral-derivative (PID) controller is used for overhead crane positioning and proportional-derivative (PD) controller for load oscillation. New time-domain performance criterion function is used in particle swarm optimization (PSO) algorithm for the tuning of the PID-PD controller rather than the general performance criteria using error of the system. …”
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  12. 12
    A novel hybrid metaheuristic algorithm for short term load forecasting

    A novel hybrid metaheuristic algorithm for short term load forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

    Published 2017
    “…With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). …”
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  13. 13
    An application barnacles mating optimizer for forecasting of full load electrical power output

    An application barnacles mating optimizer for forecasting of full load electrical power output by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Bariah, Yusob, Ferda, Ernawan

    Published 2020
    “…In this study, a rather new meta-heuristic algorithm is employed in full load electrical power output forecasting viz. …”
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  14. 14
    Evolutionary Programming (EP) based technique for secure point identification with load shedding technique in power transmission / Abdul Rahman Minhat

    Evolutionary Programming (EP) based technique for secure point identification with load shedding technique in power transmission / Abdul Rahman Minhat by Minhat, Abdul Rahman

    Published 2008
    “…The study was initiated by the development of new algorithm for automatic voltage stability assessment (AVSA). …”
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  15. 15
    Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids

    Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids by Abduh Kaid, Monir Abdullah

    Published 2009
    “…Recursive numerical equations are derived to find the optimal workload assigned to the grid node. The closed form solutions are derived for the optimal load allocation. …”
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  16. 16
    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH

    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH by Norhafidzah, Mohd Saad

    Published 2021
    “…The load flow patterns will significantly have affected when uncertain PV generation – load models are considered into the power flow algorithm. …”
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  17. 17
    A New Swarm Intelligence Approach for Optimal Chiller Loading for Energy Conservation

    A New Swarm Intelligence Approach for Optimal Chiller Loading for Energy Conservation by M. H., Sulaiman, Hassan, Ibrahim, Hamdan, Daniyal, Mohd Rusllim, Mohamed

    Published 2014
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  18. 18
    Linear optimization method by using a new iterative algorithm for available transfer capability (ATC)

    Linear optimization method by using a new iterative algorithm for available transfer capability (ATC) by Hojabri, Mojgan, Hizam, Hashim, Mariun, Norman, Abdullah, Senan Mahmood

    Published 2009
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  19. 19
    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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  20. 20
    Hybrid Metaheuristic Algorithm for Short Term Load Forecasting

    Hybrid Metaheuristic Algorithm for Short Term Load Forecasting by Zuriani, Mustaffa, M. H., Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

    Published 2016
    “…With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). …”
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