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  1. 1

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

    Published 2016
    “…The works related to swarm intelligence algorithms include the development of the algorithm itself, its modification and improvisation as well as its application in solving global optimisation problems. …”
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  2. 2

    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 aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. …”
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    Improved Bat Algorithm for faster convergence in solving optimisation problem by Ramli, Mohamad Raziff

    Published 2021
    “…The results show that the IBA performs better than Harmony Search (HS), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). …”
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    Particle Swarm Optimisation with Improved Learning Strategy by Wei , Hong Lim, Isa, Nor Ashidi Mat

    Published 2015
    “…In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. …”
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  7. 7

    Hybrid particle swarm optimization algorithm with fine tuning operators by Murthy, G.R., Arumugam, M.S., Loo, C.K.

    Published 2009
    “…This paper introduces a new approach called hybrid particle swarm optimization like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. …”
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  8. 8

    An improved leader particle swarm optimisation algorithm for solving flexible ac transmission systems optimisation problem in power system by Jordehi, Ahmad Rezaee

    Published 2014
    “…The results of applying improved leader PSO to IEEE 14 bus power system shows its significant outperformance over six other optimisation algorithms including conventional PSO, mutated PSO, enhanced PSO, harmony search,genetic algorithm and gravitational search algorithm. …”
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  9. 9

    Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system : a comparative assessment by M. F., Masrom, N. M. A., Ghani, M. O., Tokhi

    Published 2021
    “…This paper presents investigations into the development of an interval type-2 fuzzy logic control (IT2FLC) mechanism integrated with particle swarm optimization and spiral dynamic algorithm. …”
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  10. 10

    Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System by Abidin, Zulkifli Zainal

    Published 2013
    “…Therefore, the usage of a swarming robotic system is proposed. In this thesis, a simple framework and methodology in developing a bio-inspired algorithm for cooperative swarming robotic application has been developed. …”
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  11. 11

    Metaheuristic algorithms applied in ANN salinity modelling by Khudhair, Zahraa S., Zubaidi, Salah L., Dulaimi, Anmar, Al-Bugharbee, Hussein, Muhsen, Yousif Raad, Putra Jaya, Ramadhansyah, Mohammed Ridha, Hussein, Raza, Syed Fawad, Ethaib, Saleem

    Published 2024
    “…The CPSOCGSA performance was evaluated by various single-based ones, including multi-verse optimiser (MVO), marine predator's optimisation algorithm (MPA), particle swarm optimiser (PSO), and the slim mould algorithm (SMA). …”
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    A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem by Ong, Kok Meng

    Published 2021
    “…CPA improved over the particle swarm optimisation algorithm in terms of the best fitness value by 69.40-95.99% in the optimisation of the robotic arm. …”
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  14. 14

    INTELLIGENT MODELLING OF GRADIENT FLEXIBLE PLATE STRUCTURE UTILISING HYBRID EVOLUTIONARY ALGORITHM by Muhammad Hasbollah, Hassan

    Published 2023
    “…First, evolutionary algorithms, namely particle swarm optimisation (PSO) and grey wolf optimisation (GWO) were used in developing GFPS dynamic model and their performances were compared. …”
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    Development of a particle swarm optimisation model for estimating the homogeneity of a mixture inside a newly designed CNG-H2-AIR mixer for a dual fuel engine: an experimental and... by Mahmood, Hussein Adel, Adam, Nor Mariah, Sahari, Barkawi, Masuri, Siti Ujila

    Published 2018
    “…The basis of the proposal model (empirical equation) is from the best values determined for the unknown constant F so that the equation for UI estimation could be formulated. The particle swarm optimisation (PSO) algorithm was used to solve an optimisation problem and achieve this outcome. …”
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    Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique by Lai V., Huang Y.F., Koo C.H., Ahmed A.N., Sherif M., El-Shafie A.

    Published 2024
    “…The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. …”
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    Gait identification and optimisation for amphi-underwater robot by using ant colony algorithm by Mohd Yusof, Muhammad Syafiq, Toha @ Tohara, Siti Fauziah

    Published 2019
    “…For the optimization, the robot will travel from one specific point to another with the predefined position within optimized gait and fastest time by using Ant Colony Optimization (ACO) technique. The algorithm being compared, between Ant Colony Algorithm (ACO) and the Particle Swarm Optimisation (PSO) in terms of time and distance. …”
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  20. 20

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

    Published 2024
    “…This research employed a modified version of the Design Science Research Methodology (DSRM), streamlined into five stages: problem identification, theoretical study, framework development, evaluation, and reporting. The developed framework, grounded in fault-based testing theory, comprises three key components: inputs, prioritization factors, and a prioritization algorithm. …”
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