Search Results - (( using optimisation based algorithm ) OR ( parameter optimization search algorithm ))
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Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
Published 2018“…In this research, swarming intelligence is used to solve optimisation problem. Grey Wolf Optimizer and Dragonfly Algorithm were chosen. …”
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A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application
Published 2011“…The optimal simulation parameters can be used for the real application. …”
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A comparative evaluation of PID-based optimisation controller algorithms for DC motor
Published 2023“…Controllers; DC motors; Electric control equipment; Particle swarm optimization (PSO); Proportional control systems; Three term control systems; Two term control systems; Backtracking search algorithms; Comparative analysis; Comparative evaluations; Controller algorithm; Industrial activities; Optimum parameters; Particle swarm optimisation; Proportional integral derivative controllers; Electric machine control…”
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Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System
Published 2014“…Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization(PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. …”
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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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Enhancing harmony search parameters based on step and linear function for bus driver scheduling and rostering problems
Published 2018“…Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. …”
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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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Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
Published 2023“…Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. …”
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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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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. …”
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Modelling and optimisation of cooling-slope parameters of magnesium AZ91D using improvement multi-objective Jaya approach for predicted feedstock performance
Published 2024“…The results show that the hybrid MOJaya has improved the prediction of feedstock using optimal CS parameters.…”
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Adaptive Spiral Dynamics Metaheuristic Algorithm for Global Optimisation with Application to Modelling of a Flexible System
Published 2016“…A linear parametric modelling approach is utilised with an autoregressive model with exogenous inputs (ARX) structure for a flexible system. The proposed algorithm is then used to optimise parameters of the ARX structure. …”
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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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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Hence, the algorithm must overcome the problem of dynamic updates in the internal parameters or counter the concept drift. …”
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Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer
Published 2023“…A metaheuristic algorithm is introduced to optimise the hyperparameters. …”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm
Published 2024“…Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. …”
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A Study On The Application Of Gravitational Search Algorithm In Optimizing Stereo Matching Algorithm’s Parameters For Star Fruit Inspection System
Published 2018“…Benchmarking has done by comparing the result obtained with the previous literature that implements Particle Swarm Optimization. The result indicates that the application of Gravitational Search Algorithm as parameters tuner for stereo matching’s parameters tuning is essentially on par with the Particle Swarm Optimization Algorithm.…”
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Heterogenous adaptive ant colony optimization with 3-opt local search for the travelling salesman problem
Published 2020“…The majority of optimization algorithms require proper parameter tuning to achieve the best performance. …”
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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting
Published 2020“…However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. …”
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