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  1. 1

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…In order to learn the optimized states-action policy the self-learning algorithm is developed using hybrid AI algorithm by combining unsupervised weightless neural network, which employs AUTOWiSARD and reinforcement learning algorithm, which employs Q-learning. …”
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    Thesis
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    Pengoptimuman algoritma pengesanan batu loncatan dalam sistem pengesanan pencerobohan by Omar, Mohd. Nizam

    Published 2005
    “…In this research, special Top-Down methodology has been used to optimize the stepping stone algorithm. It is achieved by analyzing five stepping stone algorithms, in which each algorithm is divided into three main parts. …”
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  3. 3

    Predicting attackers of online shaming using ant colony optimization / Noor Shafiqa Fazlien Mohamad Fauzi by Mohamad Fauzi, Noor Shafiqa Fazlien

    Published 2020
    “…In order to predict the attackers of online shaming, Ant Colony Optimization Algorithm will be used and it will be compared with J48 algorithm. …”
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    Newton competitive genetic algorithm method for optimization the production of biochemical systems by Ismail, Mohd Arfian, Ab Razak, Kirahman, Kohbalan, Moorthy, Mezhuyev, Vitaliy, Kasim, Shahreen, Ibrahim, Ashraf Osman

    Published 2018
    “…In this work, the optimization of biochemical systems production is performed by using a hybrid method of Newton competitive genetic algorithm is presented. …”
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    Article
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    A Chameleon algorithm for solving economic dispatch problem in microgrid system by Zahraoui Y., Alhamrouni I., Mekhilef S., Kor�tko T., Jusoh A., Sutikno T.

    Published 2024
    “…The obtained results from the simulation are compared with the conventional metaheuristic algorithms which have been used in previous studies, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC). …”
    Article
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    Newton competitive genetic algorithm method for optimization the production of biochemical systems by Mohd Arfian, Ismail, Kirahman, Ab Razak, Kohbalan, Moorthy, Mezhuyev, Vitaliy, Shahreen, Kasim, Ashraf Osman, Ibrahim

    Published 2018
    “…In this work, the optimization of biochemical systems production is performed by using a hybrid method of Newton competitive genetic algorithm is presented. …”
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    Article
  9. 9

    Power system intentional islanding for different contingency scenarios using discrete optimization technique by Nur Zawani Binti Saharuddin

    Published 2023
    “…The performance of the proposed MDEP algorithm was evaluated using the IEEE 30-bus, IEEE 39-bus, and IEEE 118-bus test systems. …”
    text::Thesis
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    A Newton Cooperative Genetic Algorithm Method for In Silico Optimization of Metabolic Pathway Production by Mohd Arfian, Ismail, Safaai, Deris, Mohd Saberi, Mohamad, Afnizanfaizal, Abdullah

    Published 2015
    “…The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. …”
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    Article
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    Temperature control of a pilot plant reactor system using a genetic algorithm model‐based control approach by Wahab, Ahmad Khairi Abdul, Hussain, Mohd Azlan, Omer, Rosli

    Published 2007
    “…The work described in this paper aims at exploring the use of an artificial intelligence technique, i.e. genetic algorithm (GA), for designing an optimal model-based controller to regulate the temperature of a reactor. …”
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    Article
  14. 14

    Bacterial foraging optimization algorithm for optimal load shedding in power systems / Wan Nur Eliana Afif Wan Afandie by Wan Afandie, Wan Nur Eliana Afif

    Published 2016
    “…By using Bacterial Foraging Optimization Algorithm (BFOA) technique, for each types of load shedding, the simulations are tested on IEEE 30-bus system for 3 and 5 load shedding locations. 3 different load conditions and one line outage cases are tested for 3 objective function and 3 multi-objective functions. …”
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  15. 15

    Particle swarm optimization technique of current tracking controller for electric power-assisted steering system by Bahmanshiri, Adel Amiri

    Published 2015
    “…The simulation for the whole EPAS system is implemented by Matlab/Simulink. In this case, three test procedure are done to show the performance of current tracking controller in different situations, also the current tracking performance with Particle Swarm Optimization (PSO)-PID controller compared to previous research that used Ant Colony Optimization (ACO)-PID controller [1] to show the percentage of error between reference motor current and actual motor current of proposed PSO optimization algorithm with 0.023% is much less than previous worked (ACO optimization algorithm) with 4.76%.So the proposed control strategy can improve otor current tracking performance in electric powers assisted steering (EPAS) systems.…”
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    Intelligent active force control of a robotic arm using genetic algorithm by Mailah, Musa, Wong, Min Yee, Jamaluddin, Hishamuddin

    Published 2002
    “…The results show that the GA used in the study being a stochastic and global optimizer successfully computes appropriate IN value to effect the control action. …”
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    Article
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    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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    A new routing mechanism for energy-efficient in bluetooth mesh-low power nodes based on wireless sensor network by Almuslehi, Hussein Saad Mohammed

    Published 2023
    “…The multi-criterion energyefficient routing mechanism uses the ACO algorithm, inspired by ants' foraging behaviour. …”
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