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  1. 1

    Modified Parameters of Harmony Search Algorithm for Better Searching by Nur Farraliza, Mansor, Abas, Z.A, Shibghatullah, A.S., Rahman, A.F.N.A

    Published 2017
    “…The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. …”
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    Conference or Workshop Item
  2. 2

    Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data by Mohd Amin, Nor Azrita, Adam, Mohd Bakri, Ibrahim, Noor Akma

    Published 2013
    “…The Multiple-try Metropolis (MTM) algorithm is the new alternatives in the field of Bayesian extremes for summarizing the posterior distribution. …”
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    Conference or Workshop Item
  3. 3

    Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system by Krishnan P.S., Kiong T.S., Koh J., Yap D.

    Published 2023
    “…In this paper, an embedded parallel and distributed genetic algorithm (EPDGA) with dynamic parameter setting on a multiprocessor system is proposed. …”
    Conference paper
  4. 4

    Enhancing harmony search parameters based on step and linear function for bus driver scheduling and rostering problems by Mansor, Nur Farraliza

    Published 2018
    “…Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. …”
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    Thesis
  5. 5

    Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt by Shaffiei, Zatul Alwani

    Published 2021
    “…In the standard HS, the value of bandwidth (BW) parameter was static, while in this research, a dynamic set of bandwidth (BW2) value was formed based on constraints (problem domain). …”
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    Thesis
  6. 6

    Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

    Published 2015
    “…The second part of the study is on the confidence intervals (CI) for the concentration parameter in von Mises distribution. Several methods in constructing the CI for the concentration parameter are proposed including CI based on circular population, CI based on the asymptotic distribution of ˆ , CI based on the distribution of 휃 and 푅 and also CI based on bootstrap-t method. …”
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    Thesis
  7. 7

    A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

    Published 2020
    “…Input parameters, the number of fireflies, and the number of function evaluations were determined before the implementation of the firefly algorithm to solve formulated problems. …”
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    Article
  8. 8
  9. 9

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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    Thesis
  10. 10

    A new HMCR parameter of harmony search for better exploration by Mansor, N.F., Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S.

    Published 2016
    “…As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. …”
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    Conference or Workshop Item
  11. 11

    A new HMCR parameter of harmony search for better exploration by Nur Farraliza, Mansor, Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S

    Published 2015
    “…As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. …”
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    Conference or Workshop Item
  12. 12

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…In the proposed ssSKF algorithm, the initialisation parameters are not constants, but they are produced by random numbers taken from a normal distribution in the range of 0, 1, thus excluding them from tuning requirement. …”
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    Article
  13. 13

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…In the proposed ssSKF algorithm, the initialisation parameters are not constants, but they are produced by random numbers taken from a normal distribution in the range of 0, 1, thus excluding them from tuning requirement. …”
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    Article
  14. 14

    Peak pressure analysis of foot plantar distribution based on image processing algorithm by Sabry, Ali Hussein

    Published 2018
    “…The proposed image processing that based on the related parameters. Thus, the image information of the pressure sensor can solve the balancing problem for those who have a problem during standing and walking.…”
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    Thesis
  15. 15

    Optimal power flow based on fuzzy linear programming and modified Jaya algorithms by Alzihaymee, Warid Sayel Warid

    Published 2017
    “…Finally, two novel hybrid optimization algorithms namely, FLP-QOJaya algorithm for single objective OPF problems and MFLP-QOMJaya algorithm for MOOPF problems are proposed. …”
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    Thesis
  16. 16

    The comparison of tardiness in scheduling techniques for task distribution using grid simulation tool by Stapa @ Mustapa, Muhamad Azhar

    Published 2008
    “…The complexity of scheduling problem increases with the size of the Grid. The goal of this project is to test on tardiness parameter in local search based algorithms. …”
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    Thesis
  17. 17

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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    Thesis
  18. 18

    Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

    Published 2006
    “…A solution to this problem is using the Markov Chain Monte Carlo method namely Gibbs sampler algorithm. …”
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    Thesis
  19. 19

    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

    Published 2020
    “…The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. …”
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    Final Year Project
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