Search Results - (( based optimization approach algorithm ) OR ( using optimisation based algorithm ))
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Modelling of optimal placement and sizing of battery energy storage system using hybrid whale optimization algorithm and artificial immune system for total system losses reduct...
Published 2023“…First, two approaches, namely two-step optimisation approach and simultaneous optimisation approach, were incorporated to obtain optimal locations and sizing of BESS. …”
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A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application
Published 2011“…Present paper intends to provide a detailed description of a new bio-inspired Metaheuristic Algorithm. Based on the detailed study of the Drosophila, the flowchart behaviour for the algorithm, code implementation, methodologies and simulation analysis, a novel Fly Optimization Algorithm (FOA) approach is presented. …”
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Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
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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Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network
Published 2022“…Finally, an AHP was integrated with FA to form Firefly Analytical Hierarchy Algorithm (FAHA) to automatically calculate the weight of each objective function based on the load flow outputs followed by the optimisation process. …”
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An improved particle swarm optimization algorithm for data classification
Published 2023“…Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. …”
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Dual-head marking performance optimisation via evolutionary solutions
Published 2023“…This paper presents a new approach to optimise the performance of a multi-head marking system in terms of its marking speed This processing method named as MMA (Molecular Marking Optimisation algorithm) will adopt the use of Genetic Algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
Published 2020“…Technically, BFOA has been applied as supplementary algorithm for optimizing weight, parameters for other classifier algorithms and selecting optimised features for other classifiers. …”
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Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system : a comparative assessment
Published 2021“…It is shown that the particle swarm optimization-based control mechanism performs better than the spiral dynamic algorithm-based control in terms of system stability, disturbance rejection and reduce noise. …”
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Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation
Published 2024“…Unlike benchmark algorithms that rely on static VM selection or post-hoc relocation of cloudlets, the EDLB algorithm dynamically identifies optimal cloudlet placement in real-time. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Optimisation model for scheduling MapReduce jobs in big data processing / Ibrahim Abaker Targio Hashem
Published 2017“…In this study, we aim to optimize task scheduling and resource utilization using an evolutionary algorithm based on the proposed completion time and monetary cost of cloud service models. …”
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Multi-objective optimisation of assembly line balancing type-e problem with resource constraints
Published 2016“…In this research, a Genetic-based Algorithm was used as an optimisation approach. …”
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An enhanced support vector regression -African Buffalo optimisation algorithm for electricity time series forecasting
Published 2023“…This study proposes a hybrid approach combining SVR and the African Buffalo Optimization (ABO) algorithm. …”
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Manufacturing process planning optimisation in reconfigurable multiple parts flow lines
Published 2008“…Findings: The results indicate that the two genetic algorithms are able to converge to optimal solutions in reasonable time. …”
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Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
Published 2017“…This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoe network (VANET) by optimising the network coding (NC) using Genetic Algorithm and Particle Swarm Optimisation (GA-PSO). …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail
Published 2024“…In this study, a new optimisation algorithm termed Hybrid Evolutionary-Barnacles Mating Optimisation (HEBMO) was initially formulated to solve optimisation problems. …”
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Application of a primal-dual interior point algorithm using exact second order information with a novel non-monotone line search method to generally constrained minimax optimizatio...
Published 2008“…This work presents the application of a primal-dual interior point method to minimax optimisation problems. The algorithm differs significantly from previous approaches as it involves a novel non-monotone line search procedure, which is based on the use of standard penalty methods as the merit function used for line search. …”
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Investigating a round robin strategy over multi algorithms in optimising the quality of university course timetables
Published 2023“…The performance of the approach is tested with over two sets of benchmark datasets, that is, enrolment-based course timetabling and curriculum-based course timetabling (UD1) in comparison with a set of state-of-the-art methods from the literature. …”
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