Search Results - adoption from discrete optimization algorithm

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  1. 1
    Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling

    Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling by Anuar, Nurul Izah

    Published 2022
    “…It is also discovered that the hybrid-discrete MOPSO (HD-MOPSO) algorithm manages to obtain higher values in the performance metrics consisting of non-dominance ratio and hypervolume compared to the competing algorithms. …”
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  2. 2
    Optimal input features selection of wavelet-based EEG signals using GA

    Optimal input features selection of wavelet-based EEG signals using GA by Mohd. Daud, Salwani, Yunus, Jasmy

    Published 2004
    “…We present a method of selecting optimal input features from wavelet coefficients of electroencephalogram (EEG) signals. …”
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  3. 3
    Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation

    Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation by M. F. F., Ab Rashid

    Published 2013
    “…The performance of the MODPSO algorithm is finally validated using artificial problems from the literature and real-world problems from assembly products.…”
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  4. 4
    Design Of Perturbative Hyper-Heuristics For Combinatorial Optimisation

    Design Of Perturbative Hyper-Heuristics For Combinatorial Optimisation by Choong, Shin Siang

    Published 2019
    “…Combinatorial optimisation is an area which seeks to identify optimal solution(s) from a discrete solution search space. …”
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  5. 5
    Evacuation routing optimizer (EROP) / Azlinah Mohamed … [et al.]

    Evacuation routing optimizer (EROP) / Azlinah Mohamed … [et al.] by Mohamed, Azlinah, Yusoff, Marina, Ariffin, Junaidah, Shamsudin, Siti Maryam

    Published 2011
    “…For EVAP, discrete particle position is proposed to support the implementation of discrete particle swarm optimization called myDPSOVAP-A. …”
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    Research Reports
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  6. 6
    A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

    A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem by Kamal Z., Zamli, Fakhrud, Din, Ahmed, Bestoun S., Bures, Miroslav

    Published 2018
    “…The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. …”
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  7. 7
    Designing substitution boxes based on chaotic map and globalized firefly algorithm

    Designing substitution boxes based on chaotic map and globalized firefly algorithm by Ahmed, Hussam Alddin Shihab

    Published 2019
    “…The first variation of FA is called chaotic firefly algorithm (CFA), which was initialized using discrete chaotic map to enhance the algorithm to start the search from good positions. …”
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  8. 8
    Deep Reinforcement Learning For Control

    Deep Reinforcement Learning For Control by Bakar, Nurul Asyikin Abu

    Published 2021
    “…However, determining the essential behavioral outputs for autonomous driving vehicle systems or selecting the optimal output features to learn from them is not easy. …”
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  9. 9
    Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia

    Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

    Published 2019
    “…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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  10. 10
    Nonlinear dynamic system identification and control via self-regulating modular neural network

    Nonlinear dynamic system identification and control via self-regulating modular neural network by Kiong, L.C., Rajeswari, M., Rao, M.V.C.

    Published 2003
    “…In order to avoid an over-fitting problem, the SGMN deploys a Redundant Experts Removal Algorithm to remove the redundant local experts from the network. …”
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  11. 11
    The classification of wink-based eeg signals by means of transfer learning models

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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