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  1. 1

    Bats echolocation-inspired algorithms for global optimisation problems by Nafrizuan, Mat Yahya

    Published 2016
    “…Swarm intelligence algorithms, are among popular metaheuristic methods, developed and inspired by the collective behaviour of swarms that have attracted significant attention of researchers. …”
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    Thesis
  2. 2

    PID TUNING OF DC MOTOR USING SWARM ITELLIGENCE ALGORITHM by Hasdi Aimon, Arhimny

    Published 2012
    “…In this project, Particle Swarm Optimization (PSO) as one of Swarm Intelligence Algorithm based has proposed to be integrated with PID (Proportional, Integral, Derivative) Controller in order to achieve optimal tuning method. …”
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    Final Year Project
  3. 3

    Predictive modeling of condominium prices using a Particle Swarm Optimization-Random Forest approach / Che Wan Sufia Che Wan Samsudin by Che Wan Samsudin, Che Wan Sufia

    Published 2025
    “…In general, the condominium price prediction model developed using the Particle Swarm Optimization-Random Forest approach has high value for all real estate sector stakeholders, such as legal professionals, investors, and real estate developers, as it provides accurate price forecasts and practical insights.…”
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    Thesis
  4. 4

    Modelling and Optimization of Asymmetric Vehicle Routing Problem Using Particle Swarm Optimization Algorithm by Muhamad Rozikin, Kamaluddin, M. F. F., Ab Rashid

    Published 2021
    “…The optimization results indicated that this algorithm able to offer good solutions with the best answer for the practical problem. …”
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    Conference or Workshop Item
  5. 5

    Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm by Hussain, Azham, Surendar, A., Clementking, A., Kanagarajan, Sujith, Ilyashenko, Lubov K.

    Published 2018
    “…The main goal of this research work is to propose the novel practical models to predict the BI through particle swarm optimization (PSO) and imperialism competitive algorithm (ICA). …”
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    Article
  6. 6

    Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization by Mousavi, M., Yap, Hwa Jen, Musa, S.N., Tahriri, F., Md Dawal, Siti Zawiah

    Published 2017
    “…In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. …”
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    Article
  7. 7

    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

    Published 2024
    “…The contributions of this study straddle research perspectives of enhancing Regression Testing with Particle Swarm-Artificial Bee Colony Algorithm, and practical perspectives by providing software testing practitioners the TCP framework that can facilitate and accelerate the production of high-quality software products by revealing faults early and reducing time, cost, and human efforts through automation.…”
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    Thesis
  8. 8

    An optimal load shedding approach for transmission networks using Particle Swarm Optimization (PSO) technique / Mohd Farid Mohd Rawi by Mohd Rawi, Mohd Farid

    Published 2009
    “…This thesis presents an optimal load shedding approach for transmission networks using Particle Swarm Optimization (PSO). The study involves the development of PSO algorithm and engine to address load shedding in loss minimization. …”
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    Thesis
  9. 9

    A sequential handwriting recognition model based on a dynamically configurable convolution recurrent neural network and hybrid salp swarm algorithm by Ahmed Ali Mohammed, Al-saffar

    Published 2024
    “…The built DCCRNN is based on the Salp Swarm optimization Algorithm (SSA), a processor that given a particular dataset will find the best CRNN’s structure and hyperparameters. …”
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    Thesis
  10. 10

    Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost by Chen, Suihai, Bong, Chih How, Chiu, Po Chan

    Published 2024
    “…Under the background of big data, it is of practical significance to prevent loan risk by the machine learning algorithm. …”
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    Article
  11. 11

    An improved particle swarm optimization (PSO) based MPPT for PV With reduced steady-state oscillation by Ishaque, K., Salam, Z., Amjad, M., Mekhilef, Saad

    Published 2012
    “…This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steadystate oscillation (to practically zero) once the maximum power point (MPP) is located. …”
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    Article
  12. 12

    Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty by Liu, Lihua

    Published 2024
    “…Given the NP-Hard nature of the three models proposed in this thesis, two metaheuristic algorithms have been developed. A hybrid Particle Swarm Optimization-Bacterial Foraging Algorithm is developed for solving the single objective LIRP model. …”
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    Thesis
  13. 13

    A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS by Mousavi, M., Yap, Hwa Jen, Musa, S.N., Md Dawal, Siti Zawiah

    Published 2017
    “…A fuzzy hybrid GA-PSO (genetic algorithm – particle swarm optimization) algorithm was developed to optimize the model. …”
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    Article
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    Standard equations for predicting the discharge coefficient of a modified high-performance side weir by Zaji, Amir Hossein, Bonakdari, Hossein, Shamshirband, Shahaboddin

    Published 2017
    “…The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the equations. …”
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    Article
  16. 16

    Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

    Published 2021
    “…Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. …”
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    Article
  17. 17

    Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

    Published 2021
    “…Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. …”
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    Article
  18. 18

    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
    “…Genetic Algorithm (GA), Ant Colony (AC), Simulated Annealling (SA), Particle Swarm Optimization, and Harmony Search Algorithm (HS) as their basis in an effort to generate the most optimal solution. …”
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    Article
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    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

    Published 2015
    “…The scripting language is designed and developed based on the algorithm structure that is defined in the proposed implementation frameworks with the dynamic parameterization. …”
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    Thesis