Search Results - (( parameter optimization strategy algorithm ) OR ( using solution learning algorithm ))

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  1. 1
    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing

    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

    Published 2015
    “…Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. …”
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  2. 2
    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
    “…The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. …”
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  3. 3
    A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites

    A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

    Published 2019
    “…Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. …”
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  4. 4
    Optimal power flow based on fuzzy linear programming and modified Jaya algorithms

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

    Published 2017
    “…In the proposed novel QOJaya algorithm, an intelligence strategy, namely, quasi-oppositional based learning (QOBL) is incorporated into the basic Jaya algorithm to enhance its convergence speed and solution optimality. …”
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  5. 5
    Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer

    Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer by Ahmadipour, Masoud, Murtadha Othman, Muhammad, Bo, Rui, Sadegh Javadi, Mohammad, Mohammed Ridha, Hussein, Alrifaey, Moath

    Published 2024
    “…The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. …”
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  6. 6
    Inertia weight strategies in GbLN-PSO for optimum solution

    Inertia weight strategies in GbLN-PSO for optimum solution by Nurul Izzatie Husna, Fauzi, Zalili, Musa

    Published 2023
    “…In the PSO algorithm, inertia weight is an important parameter to determine the searching ability of each particle. …”
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  7. 7
    Nomadic people optimizer (NPO) for large-scale optimization problems

    Nomadic people optimizer (NPO) for large-scale optimization problems by Mohamd Salih, Sinan Qahtan

    Published 2019
    “…A metaheuristic is defined as an iterative generation process which guides a subordinate heuristic through a combination of different intelligent concepts for exploring and exploiting the solution space; they employ learning strategies to structure information in order to establish efficient near-optimal solutions. …”
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  8. 8
    Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

    Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir, Mohd Falfazli, Mat Jusof, Shuhairie, Mohammad, Nurul Amira, Mhd Rizal

    Published 2019
    “…This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. …”
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  9. 9
    Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system

    Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir, Nor Maniha, Abd Ghani

    Published 2022
    “…An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. …”
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  10. 10
    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks

    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

    Published 2025
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  11. 11
    Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach

    Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach by Wan Solehah, Wan Ahmad

    Published 2022
    “…The other one is the network training’s environment optimization that is done through hyperparameter optimization by selecting and fine-tuning high impact parameters which include Optimizer, Learning Rate and Dropout to reduce error rate (loss function). …”
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  12. 12
    Unified strategy for intensification and diversification balance in ACO metaheuristic

    Unified strategy for intensification and diversification balance in ACO metaheuristic by Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani

    Published 2017
    “…This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation).The automation between the two processes is maintained using reactive search. …”
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  13. 13
    Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao

    Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao by He , Biao

    Published 2024
    “…Third, the integration of metaheuristic algorithms further ascertains the optimal blasting parameters for overbreak minimization under specific rock sections. …”
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  14. 14
    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
    “…In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. …”
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  15. 15
    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach

    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

    Published 2025
    “…Feature selection techniques, such as WrapperSubsetEval, were used to improve focus on key attributes, and parameter tuning further optimized performance. Among the three datasets analyzed (D1, D2, and D3), Dataset 3, which emphasizes psychological and emotional factors, achieved the highest accuracy and predictive performance. …”
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  16. 16
    Advancements in crop water modelling: algorithmic developments and parameter optimization strategies for sustainable agriculture: a review

    Advancements in crop water modelling: algorithmic developments and parameter optimization strategies for sustainable agriculture: a review by Sulaiman, Ahmad S. S., Wayayok, Aimrun, Aziz, Samsuzana A., Yun, Wong Mui, Leifeng, Guo

    Published 2024
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  17. 17
    Finite impulse response optimizers for solving optimization problems

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

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  18. 18
    Finite impulse response optimizers for solving optimization problems

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

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  19. 19
    Migrating Birds Optimization based Strategies for Pairwise Testing

    Migrating Birds Optimization based Strategies for Pairwise Testing by Hasneeza, L. Zakaria, Kamal Z., Zamli

    Published 2015
    “…For pairwise testing, test cases are designed to cover all possible pair combinations of input parameter values at least once. In this paper, we investigate the adoption of Migrating Birds Optimization (MBO) algorithm as a strategy to find an optimal solution for pairwise test data reduction. …”
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  20. 20
    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem

    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem by Anniza, Hamdan, San Nah, Sze, Say Leng, Goh, Kang Leng, Chiew, Wei King, Tiong

    Published 2023
    “…Other metaheuristic approaches such as genetic algorithm, differential evolution algorithm, particle swarm optimization, and ant colony optimization are still preferable to address combinatorial optimization problems. …”
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