Humans are affected by some diseases due to ageing, which raises the necessity of effective healthcare operation schemes. Such techniques are necessary to provide efficiently and cost-effectively service to patients a...
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Humans are affected by some diseases due to ageing, which raises the necessity of effective healthcare operation schemes. Such techniques are necessary to provide efficiently and cost-effectively service to patients at the proper time. The huge knowledge required to process the HC application is obtained from the developed HC technologies. Research has recently shown that artificial intelligence (AI) can facilitate extraordinary performance for various HC applications. However, recently different metaheuristic-based optimisationalgorithms have been developed to improve AI-based HC techniques' performance. Therefore, this review discusses different HC models that leverage the benefits of metaheuristicalgorithms to achieve better performance. The major goal of this review is to support the researchers seeking a better reference to develop secure and smarter metaheuristic-based HC models. These models are efficient in reducing system complexity by improving efficiency. But in the future, many openings are available to meet such requirements efficient techniques that satisfy all the existing challenges. This developed review has surveyed and summarised different challenges and future directions to understand the available challenges. This research reviews various journal publications based on the metaheuristic *** from the recent papers available in standard journals from 2015 to 2021.
The continuous exposure of excavator bucket teeth to abrasive materials during excavation causes wear, leading to frequent replacements and unplanned downtime. Therefore, developing reliable models for accurately pred...
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The continuous exposure of excavator bucket teeth to abrasive materials during excavation causes wear, leading to frequent replacements and unplanned downtime. Therefore, developing reliable models for accurately predicting bucket teeth wear is crucial for effective maintenance strategies. In this study, novel hybrid artificial intelligence models are introduced to achieve accurate predictions of excavator bucket teeth wear. These models were built on the Multilayer Perceptron Neural Network (MLPNN) and optimised using five metaheuristicoptimisation techniques: Whale optimisationalgorithm (WOA), Particle Swarm optimisation (PSO), Ant Lion optimisation (ALO), Grey Wolf optimisation (GWO), and Genetic algorithm (GA). These optimisation techniques improved the effectiveness of the MLPNN model by adjusting its weights and biases. The accuracy of the optimised models, along with the standalone MLPNN models, was assessed using six statistical indicators: coefficient of determination (R2), relative root mean square error (RRMSE), mean absolute error (MAE), correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE). Additionally, the Bayesian Information Criterion was employed to choose the top-performing predictive method. The statistical results confirmed the superior performance of the ALO-MLPNN hybrid model, which achieved the lowest error values for RMSE (0.22007), RRMSE (0.00597), MAE (0.10014), and MAPE (0.26704), alongside high values for R2 (0.99962) and R (0.99981). Additionally, ALO-MLPNN recorded the lowest Bayesian Information Criterion (BIC) value of-245.53662, reinforcing its effectiveness in predicting on-site wear of excavator bucket teeth. These findings emphasise the model's strong potential to enhance the accuracy of AI-based wear prediction systems.
The increased penetration of distributed energy resources (DERs) installed in distribution networks via power electronic converters reduces the overall rotating inertia of the power system, causing faster frequency dy...
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The increased penetration of distributed energy resources (DERs) installed in distribution networks via power electronic converters reduces the overall rotating inertia of the power system, causing faster frequency dynamics after a large disturbance. However, these DERs coupled with energy storage systems (ESSs) can be managed to provide valuable grid support functions such as fast frequency response (FFR) and assist the transmission system operator (TSO) in frequency control. In this study, single- and three-phase clusters of electric vehicles (EVs), acting as mobile-ESSs, are grouped together as virtual power plants (VPPs) to centralise the provision of FFR for the distribution system operator (DSO) whilst considering network unbalance. Furthermore, the parameters of the EV clusters within each VPP are optimised to minimise the inertia-weighed maximum frequency deviation following a disturbance whilst adhering to network security and power quality constraints. To this aim, a variant of the metaheuristic optimisation algorithm called improved harmony search is used for the optimisation process. Finally, the merits of the proposed methodology are shown with an illustrative example of an unbalanced three-phase transmission and distribution network modelled in DIgSILENT PowerFactory. The results show that clusters of EVs grouped as VPPs effectively improve frequency support via the TSO-DSO interaction.
With development of two-way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resourc...
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With development of two-way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air-conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K-medoids-based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level.
Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. H...
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Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. However, this review paper tries to focus on the historical development of algorithm structure instead of applications. This paper explains the original HS algorithm along with a selection of modified and hybrid HS methods: adaption of original operators of the basic harmony search, parameter adaption, hybrid methods, handling multi objective optimisation problems and constraint handling.
optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation te...
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optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C-3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C-3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C-3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C-3. The results illustrate that with C-3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.
Owing to its significant roles in computer vision applications, human face tracking has drawn extensive attention in recent years. Most researchers solve face tracking using particle filter, meanshift and their deriva...
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Owing to its significant roles in computer vision applications, human face tracking has drawn extensive attention in recent years. Most researchers solve face tracking using particle filter, meanshift and their derivatives. Unlike the traditional methods, in this study, face tracking is treated as an optimisation problem and a new meta-heuristic optimisationalgorithm, differential harmony search (DHS), is introduced to solve face tracking problems. We compare the speed and accuracy of the proposed method with particle filter, meanshift and improved harmony search. Experimental results show that DHS-based tracker is faster and more accurate and it is easy to handle the parameters tuning. Furthermore, to improve the reliability of tracking, multiple visual cues are applied to DHS-based tracking system and experimental results demonstrate the increased robustness achieved by fusing multiple cues.
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