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  1. 1
    Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network

    Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network by Ismail Abdullah, Kalid Abdlkader Marsal

    Published 2024
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  2. 2
    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm

    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

    Published 2018
    “…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. This is to remedy the problem of using the existing Min-Max (MM) and Decimal Scaling (DS) techniques, which have overflow weakness. …”
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  3. 3
    Performance comparison of differential evolution and particle swarm optimization in constrained optimization

    Performance comparison of differential evolution and particle swarm optimization in constrained optimization by Iwan, Mahmud, Akmeliawati, Rini, Faisal, Tarig, Al-Assadi, Hayder M.A.A.

    Published 2012
    “…There are quite numbers of modern optimization algorithms proposed in the last two decades to solve optimization problems. …”
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  4. 4
    Ant colony optimization in dynamic environments

    Ant colony optimization in dynamic environments by Chen, Fei Huang

    Published 2010
    “…Apart from the size of the optimization problem, how the swapping interval affects the dynamic optimization by the ant algorithms is also investigated. …”
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  5. 5
    A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

    A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function by Mahdi, F.P., Vasant, P., Abdullah-Al-Wadud, M., Watada, J., Kallimani, V.

    Published 2018
    “…Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4-objective problem considering ED. Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. …”
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  6. 6
    A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

    A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function by Mahdi, F.P., Vasant, P., Abdullah-Al-Wadud, M., Watada, J., Kallimani, V.

    Published 2018
    “…Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4-objective problem considering ED. Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. …”
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  7. 7
    Improving neural networks training using experiment design approach

    Improving neural networks training using experiment design approach by Chong, Wei Kean

    Published 2005
    “…There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. …”
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  8. 8
    The effect of pre-processing techniques and optimal parameters on BPNN for data classification

    The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

    Published 2015
    “…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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  9. 9
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). …”
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  10. 10
    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin

    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin by Alsahag, Ali Mohammed, Mohd Ali, Borhanuddin, Noordin, Nor Kamariah, Mohamad, Hafizal

    Published 2014
    “…In this paper, an efficient bandwidth allocation algorithm for the uplink traffic in mobile WiMAX is proposed. …”
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  11. 11
    Application of genetic algorithm methods to optimize flowshop sequencing problem

    Application of genetic algorithm methods to optimize flowshop sequencing problem by Mohd Fadil, Md Sairi

    Published 2008
    “…Genetic algorithm method was one of the methods that were widely used in solving optimization problem. …”
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    Undergraduates Project Papers
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  12. 12
    Effective downlink resource management for wimax networks

    Effective downlink resource management for wimax networks by Shareef, Zayd Ashraf Najeeb

    Published 2018
    “…Our EDRM framework involves three functions: Class-Based Scheduling (CBS) algorithm, Dynamic Bandwidth Allocation (DBA) scheme and Link Session Management (LSM) policy. …”
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  13. 13
    Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems

    Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems by Bibi Amirah Shafaa, Emambocus

    Published 2024
    “…Furthermore, the original DA is only suitable for solving continuous optimization problems. Although there is a binary version of the algorithm, it cannot be directly used for solving discrete optimization problems like the Traveling Salesman Problem (TSP). …”
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  14. 14
    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem

    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem by Anniza, Hamdan, San Nah, Sze, Say Leng, Goh, Kang Leng, Chiew, Wei King, Tiong

    Published 2023
    “…The evolutionary algorithm has been extensively used to solve a range of combinatorial optimization problems. …”
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  15. 15
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. …”
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  16. 16
    Modern fuzzy min max neural networks for pattern classification

    Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

    Published 2019
    “…To build an efficient classifier model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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  17. 17
    A discrete simulated kalman filter optimizer for combinatorial optimization problems

    A discrete simulated kalman filter optimizer for combinatorial optimization problems by Suhazri Amrin, Rahmad

    Published 2022
    “…Another type of algorithm is called numerical algorithms. These algorithms were built specifically to address numerical optimization problems. …”
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  18. 18
    Bees algorithm for Forest transportation planning optimization in Malaysia

    Bees algorithm for Forest transportation planning optimization in Malaysia by Jamaluddin, Jamhuri, Kamarudin, Norizah, Ismail, Mohd Hasmadi, Ahmad, Siti Azfanizam

    Published 2021
    “…Algorithm is widely used in various areas due to its ability to solve classes of problems. …”
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  19. 19
    Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem

    Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem by Ismail, Ibrahim, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2015
    “…Later, binary gravitational search algorithm (BGSA) is designed to solve discrete optimization problems. …”
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    Conference or Workshop Item
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  20. 20
    Optimal design of step – cone pulley problem using the bees algorithm

    Optimal design of step – cone pulley problem using the bees algorithm by Yusof, Noor Jazilah, Kamaruddin, Shafie

    Published 2021
    “…Nowadays, there is a lot of optimization algorithms available to find an optimal solution in engineering problems. …”
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