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  1. 1
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Besides, some real data sets were examined to validate the proposed algorithm. Empirical evidences based on simulated data sets indicated that the proposed modified k-means algorithm is able to recognise the optimum number of clusters for uncorrelated data sets. …”
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  2. 2
    A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator

    A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2021
    “…The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. …”
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  3. 3
    A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and Swap Operator

    A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and Swap Operator by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2021
    “…The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. …”
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  4. 4
    Simulated kalman filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem

    Simulated kalman filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2022
    “…Most metaheuristic algorithms are designed for continuous optimization problem. …”
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  5. 5
    A Modified Gravitational Search Algorithm for Discrete Optimization Problem

    A Modified Gravitational Search Algorithm for Discrete Optimization Problem by Zuwairie, Ibrahim, Zulkifli, Md. Yusof, Shahdan, Sudin, Sophan Wahyudi, Nawawi, Amar Faiz, Zainal Abidin, Muhammad Arif, Abdul Rahim, Kamal, Khalil

    Published 2014
    “…This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. …”
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  6. 6
    An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion

    An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion by Haque, Ariful

    Published 2018
    “…We have chosen four neighborhood based algorithms which are commonly used in optimization problems and divided them in newly implemented and re-implemented category. …”
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  7. 7
    A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution

    A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution by Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri

    Published 2018
    “…This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. …”
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  8. 8
    New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems

    New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems by Nor Shuhada, Ibrahim

    Published 2024
    “…In the fourth phase, the newly developed algorithm undergoes testing on the formulated ROOPs and compared to several contemporary optimizer algorithms. …”
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  9. 9
    Optimal power flow using the Jaya algorithm

    Optimal power flow using the Jaya algorithm by Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri

    Published 2016
    “…Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. …”
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  10. 10
    Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems

    Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems by Yusof, Zulkifli Md., Ibrahim, Zuwairie, Adam, Asrul, Azmi, Kamil Zakwan Mohd, Ab. Rahman, Tasiransurini, Muhammad, Badaruddin, Ab. Aziz, Nor Azlina, Abd Aziz, Nor Hidayati, Mokhtar, Norrima, Shapiai, Mohd Ibrahim, Muhammad, Mohd Saberi

    Published 2018
    “…Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. …”
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  11. 11
    A harmony search-based learning algorithm for epileptic seizure prediction

    A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

    Published 2016
    “…Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in terms of sensitivity.…”
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  12. 12
    Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems

    Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems by Zuwairie, Ibrahim, Zulkifli, Md. Yusof, Asrul, Adam, Kamil Zakwan, Mohd Azmi, Tasiransurini, Ab Rahman, Badaruddin, Muhammad, Nor Azlina, Ab. Aziz, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Mohd Saberi, Mohamad

    Published 2018
    “…Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. …”
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  13. 13
    Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem

    Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2022
    “…There were also attempts to hybridize SKF with other famous algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Sine Cosine Algorithm (SCA) to improve its performance. …”
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  14. 14
    A modified n-th section line search in conjugate gradient methods for solving unconstrained optimization / Muhammad Imza Fakhri Jinudin

    A modified n-th section line search in conjugate gradient methods for solving unconstrained optimization / Muhammad Imza Fakhri Jinudin by Jinudin, Muhammad Imza Fakhri

    Published 2018
    “…For unconstrained optimization, line search act as a pillar for solving optimization problems. …”
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  15. 15
    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm by Abro, Abdul Ghani

    Published 2013
    “…Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. …”
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  16. 16
    Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm

    Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm by Ren Hao, Mok, Ahmad, Mohd Ashraf

    Published 2022
    “…Nevertheless, many existing optimization tools for tuning the FOPID controller, which are based on multi-agent based optimization, require large number of function evaluation in their algorithm that could lead to high computational burden. …”
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  17. 17
    Development of optimization Alghorithm for uncertain non-linear dynamical system

    Development of optimization Alghorithm for uncertain non-linear dynamical system by Abdul Aziz, Mohd. Ismail, Yaacob, Nazeeruddin, Mohd. Said, Norfarizan, Hamzah, Nor Hazadura

    Published 2004
    “…Based on the results of these simulations, we compared the number of iterations needed by each algorithm to arrive at the optimal solution and the CPU time taken for each algorithm to execute the search. …”
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  18. 18
    An enhanced simulated kalman filter algorithm and its application in adaptive beamforming

    An enhanced simulated kalman filter algorithm and its application in adaptive beamforming by Lazarus, Kelvin, Nurul Hazlina, Noordin, Zuwairie, Ibrahim

    Published 2019
    “…In this paper, a population based metaheuristic algorithm named Simulated Kalman Filter with Modified Measurement (SKFMM) is proposed for adaptive beamforming application. …”
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  19. 19
    Online DE optimization for Fuzzy-PID controller of semi-active suspension system featuring MR damper

    Online DE optimization for Fuzzy-PID controller of semi-active suspension system featuring MR damper by Ahmed, Hesham, As’arry, Azizan, Hairuddin, Abdul Aziz, Hassan, Mohd Khair, Liu, Yunyun, Onwudinjo, Erasmus Cufe Ujunwa

    Published 2022
    “…In this paper, MR fluid damper with Fuzzy-PID controller is examined to be optimized using a modified DE algorithm. However, in the Fuzzy-PID controller, the fuzzy logic algorithm is used to auto-tune the PID controller, but it cannot be considered as a fully real-time controller since the fuzzy algorithm uses a previous knowledge base built offline. …”
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  20. 20
    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers

    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2022
    “…Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. …”
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