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  1. 1
    DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMZATION FRAMEWORK FOR INDUSTRIAL PROCESSES

    DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMZATION FRAMEWORK FOR INDUSTRIAL PROCESSES by GANESAN, TIMOTHY

    Published 2014
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  2. 2
    Revisiting the pheromone evaluation mechanism in the interacted multiple ant colonies optimization framework

    Revisiting the pheromone evaluation mechanism in the interacted multiple ant colonies optimization framework by Aljanaby A., Ku-Mahamud K.R., Norwawi N.Md.

    Published 2024
    Conference Paper
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  3. 3
    Interacted Multiple Ant Colonies for Search Stagnation Problem

    Interacted Multiple Ant Colonies for Search Stagnation Problem by Aljanabi, Alaa Ismael

    Published 2010
    “…An analysis study of the stagnation behaviour shows that the proposed algorithmic framework suffers less from stagnation than other ACO algorithmic frameworks.…”
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  4. 4
    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation

    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation by Rong, Li, Shari, Zalina, Ab Kadir, Mohd Zainal Abidin

    Published 2025
    “…This study systematically examined recent research trends in multi-objective optimization (MOO) for building performance from 2020 to 2024 and proposed a conceptual framework integrating intelligent algorithms, simulation tools, and climate adaptation strategies. …”
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  5. 5
    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023) by Hosseini E., Al-Ghaili A.M., Kadir D.H., Gunasekaran S.S., Ahmed A.N., Jamil N., Deveci M., Razali R.A.

    Published 2025
    “…This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. …”
    Review
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  6. 6
    An improved data classification framework based on fractional particle swarm optimization

    An improved data classification framework based on fractional particle swarm optimization by Sherwani, Fahad

    Published 2019
    “…The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. …”
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  7. 7
    Cloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning

    Cloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning by Ahmed Mohamed Ghetas, Mohamed Rezk

    Published 2017
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  8. 8
    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

    Published 2018
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  9. 9
    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

    Published 2023
    “…In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. …”
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  10. 10
    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

    Published 2023
    “…In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. …”
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  11. 11
    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

    Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Sara, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

    Published 2023
    “…In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. …”
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  12. 12
    Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation

    Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

    Published 2010
    “…Search stagnation is a serius prblem that all Ant Colony Optimization (ACO) algorithms suffer from regardless of their application domain. …”
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  13. 13
    GENETIC ALGORITHM OPTIMIZED PACKET FILTERING

    GENETIC ALGORITHM OPTIMIZED PACKET FILTERING by NURIKA, OKTA

    Published 2013
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  14. 14
    Towards Software Product Lines Optimization Using Evolutionary Algorithms

    Towards Software Product Lines Optimization Using Evolutionary Algorithms by Jamil, Muhammad Abid, K Nour, Mohamed, Alhindi, Ahmed Hasan, Awang Abu Bakar, Normi Sham, Arif, Muhammad, Muhammad Aljabri, Tareq

    Published 2019
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  15. 15
    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation

    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

    Published 2023
    “…Normally, a user takes several months of learning in order to become highly productive in using a specific object-oriented application framework. Without proper documentation, frameworks are not very usable to framework users. …”
    Conference paper
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  16. 16
    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom

    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

    Published 2015
    “…Evaluations of four different sets of applications that used the proposed implementation frameworks with dynamic parameterization have indicated the effectiveness of each tested algorithm in comparison to the single PSO and constant parameterization. …”
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  17. 17
    A Hybrid Soft Computing Framework for Electrical Energy Optimization

    A Hybrid Soft Computing Framework for Electrical Energy Optimization by Akhtar, Shamim, Muhamad Zahim, Sujod, Rizvi, Syed Sajjad Hussain

    Published 2021
    “…In this paper, a hybrid soft-computing-based framework has been proposed for intelligent energy management and optimization. …”
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  18. 18
    Multi objective bee colony optimization framework for grid job scheduling

    Multi objective bee colony optimization framework for grid job scheduling by Alyaseri, Sana, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources.…”
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  19. 19
    Analysis of the stagnation behavior of the interacted multiple ant colonies optimization framework

    Analysis of the stagnation behavior of the interacted multiple ant colonies optimization framework by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana

    Published 2011
    “…Search Stagnation is a common problem that all Ant Colony Optimization (ACO) algorithms suffer from regardless of their application domain. …”
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  20. 20
    Metaheuristic Algorithms and Neural Networks in Hydrology

    Metaheuristic Algorithms and Neural Networks in Hydrology

    Published 2024
    “…It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.…”
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