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  1. 1
    Approximate maximum clique algorithm (AMCA): A clever technique for solving the maximum clique problem through near optimal algorithm for minimum vertex cover problem

    Approximate maximum clique algorithm (AMCA): A clever technique for solving the maximum clique problem through near optimal algorithm for minimum vertex cover problem by Fayaz, Muhammad, Arshad,, Shakeel, Shah,, Abdul Salam, Shah, Asadullah

    Published 2018
    “…Background and Objective: The process of solving the Maximum Clique (MC) problem through approximation algorithms is harder, however, the Maximum Vertex Cover (MVC) problem can easily be solved using approximation algorithms. …”
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  2. 2
    Simulated Kalman Filter algorithms for solving optimization problems

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

    Published 2019
    “…Optimization is an important process in solving most engineering problems. Unfortunately, many practical optimization problems cannot be solved to optimality within reasonable computational effort. …”
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  3. 3
    Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

    Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources by Abed, Munther Hameed, Mohd Nizam Mohmad, Kahar

    Published 2022
    “…Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. …”
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  4. 4
    Generic DNA encoding design scheme to solve combinatorial problems

    Generic DNA encoding design scheme to solve combinatorial problems by Rofilde, Hasudungan

    Published 2015
    “…The complexity of combinatorial problems is classified as NP meaning that algorithms are yet to exist to efficiently solve the problem. …”
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  5. 5
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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    Final Year Project
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  6. 6
    Hybridizing guided genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources

    Hybridizing guided genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources by Abed, Munther H., Mohd Nizam, Mohmad Kahar

    Published 2023
    “…Genetic algorithm shows promising results in solving UPMR. …”
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  7. 7
    Implication of image processing algorithm in remote sensing and GIS applications

    Implication of image processing algorithm in remote sensing and GIS applications by Pathan, Mubeena, Jusoff, Kamaruzaman, Mohd Sood, Alias, Razali, Yaakob, Qureshi, Barkatullah

    Published 2011
    “…An algorithm solves the complex problems more efficiently and consistently. …”
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  8. 8
    An improved sine cosine algorithm for solving optimization problems

    An improved sine cosine algorithm for solving optimization problems by Mohd Helmi, Suid, Mohd Riduwan, Ghazali, Mohd Ashraf, Ahmad, Addie Irawan, Hashim, Raja Mohd Taufika, Raja Ismail, Mohd Zaidi, Mohd Tumari

    Published 2018
    “…Due to its simplicity and less tedious parameter tuning over other multi-agent-based optimization algorithms, Sine Cosine Algorithm (SCA) has gained lots of attention from numerous researchers for solving optimization problem. …”
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  9. 9
    Levy slime mould algorithm for solving numerical and engineering optimization problems

    Levy slime mould algorithm for solving numerical and engineering optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

    Published 2022
    “…The proposed Levy Slime Mould Algorithm (LSMA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic called Slime Mould Algorithm (SMA) for solving numerical and engineering problems. …”
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  10. 10
    Levy tunicate swarm algorithm for solving numerical and real-world optimization problems

    Levy tunicate swarm algorithm for solving numerical and real-world optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

    Published 2022
    “…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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  11. 11
    The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

    The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement by Hosseini E., Al-Ghaili A.M., Kadir D.H., Daneshfar F., Gunasekaran S.S., Deveci M.

    Published 2025
    “…SGA is used in the process of solving optimization test problems by neural networks. …”
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  12. 12
    Optimization of mycelium growth using genetic algorithm for multi-objective functions

    Optimization of mycelium growth using genetic algorithm for multi-objective functions by Muhamad Faiz, Abu Bakar

    Published 2019
    “…Trade-off between the objectives, exist for the optimization process. To solve this issues, multi-objective genetic algorithm was chosen as the methodology for this project, specifically using NSGA-ii algorithm. …”
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    Undergraduates Project Papers
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  13. 13
    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid

    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

    Published 2017
    “…SA is chosen to be hybridize with HSA for solving CBCTT because in literature, SA was successfully hybridize with HSA to solve other domain of problems. …”
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  14. 14
    Ant colony optimization in dynamic environments

    Ant colony optimization in dynamic environments by Chen, Fei Huang

    Published 2010
    “…This thesis is an investigation into the application ' of ACO for solving dynamic optimization problems. The first objective of this study is to identify which ant algorithm performs the best under a dynamic environment. …”
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  15. 15
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…Optimization problems are frequently found in various fields. The classification of estimation-based metaheuristic algorithms has been introduced for solving optimization problems. …”
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  16. 16
    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

    Published 2020
    “…A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. …”
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  17. 17
    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri

    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri by Mohd Sabri, Norlina

    Published 2020
    “…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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  18. 18
    A Kalman Filter Approach for Solving Unimodal Optimization Problems

    A Kalman Filter Approach for Solving Unimodal Optimization Problems by Zuwairie, Ibrahim, Nor Hidayati, Abdul Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Ibrahim, Shapiai, Sophan Wahyudi, Nawawi, Mohd Saberi, Mohamad

    Published 2015
    “…To evaluate the performance of the SKF algorithm in solving unimodal optimization problems, it is applied unimodal benchmark functions of CEC 2014 for real-parameter single objective optimization problems. …”
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  19. 19
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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  20. 20
    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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