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  1. 1
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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  2. 2
    SecPath: Energy efficient path reconstruction in wireless sensor network using iterative smoothing

    SecPath: Energy efficient path reconstruction in wireless sensor network using iterative smoothing by Abd, Wamidh Jwdat

    Published 2019
    “…This work uses iterative smoothing algorithm to find an alternative path with less distance and energy consumption. …”
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  3. 3
    Energy efficient path reconstruction in wireless sensor network using iPath

    Energy efficient path reconstruction in wireless sensor network using iPath by Hasan, Sazlinah, Abd, Wamidh Jwdat, Ariffin, Ahmad Alauddin

    Published 2019
    “…This work uses iterative boosting algorithm to find an alternative path with less distance and energy consumption. …”
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  4. 4
    Visdom: Smart guide robot for visually impaired people

    Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

    Published 2025
    “…Core functionalities such as path planning, autonomous movement, voice feedback, and app-to-robot communication have been thoroughly tested and optimized. …”
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    Final Year Project / Dissertation / Thesis
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  5. 5
    Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution

    Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution by Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.

    Published 2014
    “…The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. …”
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  6. 6
    The evolution and trend of chain code scheme

    The evolution and trend of chain code scheme by Wulandhari, Lily Ayu, Haron, Habibollah

    Published 2008
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  7. 7
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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  8. 8
    Global optimal analysis of variant genetic operations in solar tracking

    Global optimal analysis of variant genetic operations in solar tracking by Fam D.F., Koh S.P., Tiong S.K., Chong K.H.

    Published 2023
    “…Genetic Algorithms (GAs), Evolution Strategies (ES), Evolutionary Programming (EP) and Genetic Programming (GP) are some of the best known types of Evolutionary Algorithm (EA)where it is a class of global search algorithms inspired by natural evolution. …”
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  9. 9
    Handover procedure between macrocell and femtocell in long term evolution (LTE) network / Nurul Afzan Zakaria

    Handover procedure between macrocell and femtocell in long term evolution (LTE) network / Nurul Afzan Zakaria by Zakaria, Nurul Afzan

    Published 2013
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  10. 10
    Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

    Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Mohamed, Raihani

    Published 2018
    “…Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
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  11. 11
    Evolution of transformer health index in the form of mathematical equation

    Evolution of transformer health index in the form of mathematical equation by Azmi, Akma, Jasni, Jasronita, Azis, Norhafiz, Ab Kadir, Mohd Zainal Abidin

    Published 2017
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  12. 12
    Nature-Inspired cognitive evolution to play Ms. Pac-Man

    Nature-Inspired cognitive evolution to play Ms. Pac-Man by Tse, Guan Tan, Jason Teo, Patricia Anthony

    Published 2011
    “…Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. …”
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  13. 13
    Performance analyses of adaptive handover decision algorithm using spectrum aggregation in long term evolution - advanced network

    Performance analyses of adaptive handover decision algorithm using spectrum aggregation in long term evolution - advanced network by Usman, I. H., Nordin, N. K., Omizegba, E. E., Sali, A., Rasid, M. F. A., Hashim, F.

    Published 2021
    “…This implied that the network operating under the proposed TVWS algorithm presented low radio link decoding errors when compared with the validation algorithms MIF and CONV. …”
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    Conference or Workshop Item
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  14. 14
    The evolution of spectral analysis of surface wave method – a review

    The evolution of spectral analysis of surface wave method – a review by Mannan Mitu, Sadia Mannan Mitu, Abd. Rahman, Norinah, Mohd Taib, Aizat, Ramli, Ahmad Bukhari, Abang Hasbollah, Dayang Zulaika

    Published 2021
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  15. 15
    Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed

    Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed by Mudasir , Ahmed

    Published 2019
    “…Simulation results show that, at the high-level inverter, the proposed algorithm can easily find the feasible solutions, however, GA, PSO, bee algorithm (BA), and differential evolution (DE) face the difficulty due to less exploration capability. …”
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  16. 16
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
    “…These algorithms have succeeded with different accuracy levels, but it is still suffering from some weaknesses. …”
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  17. 17
    Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem

    Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem by Ismail, Zuhaimy, Nurhadi, Irhamah, Zainuddin, Zaitul Marlizawati

    Published 2008
    “…New algorithms, journal papers and computerized system were also developed. …”
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    Monograph
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  18. 18
    Current Developments In Lte/Lte-Advanced : Adaptive-Proportional Fair Scheduling In Lte

    Current Developments In Lte/Lte-Advanced : Adaptive-Proportional Fair Scheduling In Lte by Ismail, Mohd Khairy, Awang Md Isa, Azmi, Johal, Mohammad Syahrir, Mohd Zin, Mohd Shahril Izuan

    Published 2015
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  19. 19
    Crossover and mutation operators of genetic algorithms

    Crossover and mutation operators of genetic algorithms by Lim, Siew Mooi, Md. Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mustapha, Aida, Leong, Kuan Yew

    Published 2017
    “…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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  20. 20
    Crossover and mutation operators of genetic algorithms

    Crossover and mutation operators of genetic algorithms by Siew, Mooi Lim, Md. Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mustapha, Aida, Leong, K. Y.

    Published 2017
    “…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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