Search Results - (( evolution implementation case algorithm ) OR ( java applications optimization algorithm ))

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    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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    Conference or Workshop Item
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    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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    Final Year Project
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    Differential evolution based special protection and control scheme for contingency monitoring of transmission line overloading by Hadi, Mahmood Khalid, Othman, Mohammad Lutfi, Abdul Wahab, Noor Izzri

    Published 2017
    “…Simulation results for various N − 1 contingency conditions within the considered power system under base case load are proposed and validated with those results from the Genetic Algorithm (GA). …”
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    Conference or Workshop Item
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    A holistic review on artificial intelligence techniques for well placement optimization problem by Islam, J., Vasant, P.M., Negash, B.M., Laruccia, M.B., Myint, M., Watada, J.

    Published 2020
    “…Over the last decade, both gradient-based and gradient-free optimization methods have been implemented to tackle this problem. Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. …”
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    Article
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    Differential Evolution Based Special Protection and Control Scheme for Contingency Monitoring of Transmission Line Overloading by Othman, Mohammad Lutfi, Hadi, Mahmood Khalid, Abdul Wahab, Noor Izzri

    Published 2017
    “…Simulation results for various N − 1 contingency conditions within the considered power system under base case load are proposed and validated with those results from the Genetic Algorithm (GA). …”
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    Book Section
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    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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    Thesis
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    Sub-route reversal repair mechanism and differential evolution for urban transit network design problem by Tarajo, Buba Ahmed

    Published 2017
    “…This thesis considers the urban transit network design problem (UTNDP) focusing on the implementation of population-based metaheuristic approaches, specifically on differential evolution (DE) and particle swarm optimization (PSO). …”
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    Thesis
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    An improved firefly algorithm for optimal microgrid operation with renewable energy by Saleh, Syukur

    Published 2017
    “…It shows that the IFA obtained better results in terms of operating costs compared to FA, Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA).…”
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    Thesis
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    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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    Thesis
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    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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    Thesis
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    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…We have targeted the research on class timetabling problem. Hence, Genetic Algorithm (GA) is used as one of the most popular optimization solutions. …”
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    Thesis
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    Omega grey wolf optimizer (ωGWO) for optimization of overcurrent relays coordination with distributed generation by Noor Zaihan, Jamal

    Published 2019
    “…The GWO algorithm is tested to four different test cases which are IEEE 3 bus (consists of six OCRs), IEEE 8 bus (consists of 14 OCRs), 9 bus (consists of 24 OCRs) and IEEE 15 bus (consists of 42 OCRs) test systems with normal inverse (NI) characteristic curve for all test cases and very inverse (VI) curve for selected cases to test the flexibility of the GWO algorithm. …”
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    Thesis
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