Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedi...Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.展开更多
The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mas...The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mass is subjected to a zero-mean Gaussian white noise excitation,the main objective of this study is to maximise the efficiency of the targeted energy transfer in the system.A surrogate optimisation algorithm is proposed for this purpose and adopted for the stochastic framework.The optimisations are conducted separately for the nonlinear stiffness coefficient alone as well as for both the nonlinear stiffness and damping coefficients together.Three different optimisation cost functions,based on either energy of the system’s components or the dissipated energy,are considered.The results demonstrate some clear trends in values of the nonlinear energy sink coefficients and show the effect of different cost functions on the optimal values of the nonlinear system’s coefficients.展开更多
Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electri...Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.展开更多
Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing t...Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.展开更多
There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implem...There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.展开更多
Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters t...Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.展开更多
This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body wi...This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body with three degrees-of-freedom and its motions are analysed in time-domain using the implicit Newmark Beta technique. The mooring restoring force-excursion relationship is evaluated using quasi-static approach. MATLAB codes DATSpar and QSAML, are developed to compute the dynamic responses of truss spar platform and to determine the mooring system stiffness. To eliminate the conventional trial and error approach in the mooring system design, a numerical tool is also developed and described in this paper for optimising the mooring configuration. It has a graphical user interface and includes regrouping particle swarm optimisation technique combined with DATSpar and QSAML. A case study of truss spar platform with ten mooring lines is analysed using this numerical tool. The results show that optimum mooring system design benefits the oil and gas industry to economise the project cost in terms of material, weight, structural load onto the platform as well as manpower requirements. This tool is useful especially for the preliminary design of truss spar platforms and its mooring system.展开更多
Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has becom...Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.展开更多
A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed t...A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.展开更多
Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubin...Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubing, etc., in ultra-super critical and advanced ultra-super critical boiler applications. In the present work, laser welding process has been optimised for P92 material by using Taguchi based grey relational analysis(GRA).Bead on plate(BOP) trials were carried out using a 3.5 k W diffusion cooled slab CO_2 laser by varying laser power, welding speed and focal position. The optimum parameters have been derived by considering the responses such as depth of penetration, weld width and heat affected zone(HAZ) width. Analysis of variance(ANOVA) has been used to analyse the effect of different parameters on the responses. Based on ANOVA, laser power of 3 k W, welding speed of 1 m/min and focal plane at-4 mm have evolved as optimised set of parameters. The responses of the optimised parameters obtained using the GRA have been verified experimentally and found to closely correlate with the predicted value.? 2016 China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.展开更多
Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and fiv...Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.展开更多
Optimisation of effective design parameters to reduce tooth bending stress for an automotive transmission gearbox is presented. A systematic investigation of effective design parameters for optimum design of a five-sp...Optimisation of effective design parameters to reduce tooth bending stress for an automotive transmission gearbox is presented. A systematic investigation of effective design parameters for optimum design of a five-speed gearbox is studied. For this aim contact ratio effect on tooth bending stress by the changing of contact ratio with respect to pressure angle is analysed. Additionally, profile modification effects on tooth bending stress are presented. During the optimisation, the tooth bending stress is considered as the objective function, and all the geometric design parameters such as module, teeth number etc. are optimised under two different constraints, including tooth contact stress and constant gear centre distance. It can be concluded that higher the contact ratio results in a reduced tooth bending stress, while higher the pressure angle caused an increase in tooth bending stress and contact stress, since decreases in the contact ratio. In addition, application of positive profile modification on tooth reduces tooth bending stress. All of the obtained optimum solutions satisfy all constraints.展开更多
Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task particles.Meanwhile,these algorithms use a fixed inter-task learning probability throug...Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task particles.Meanwhile,these algorithms use a fixed inter-task learning probability throughout the evolution process.However,the parameter is problem dependent and can be various at different stages of the evolution.In this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO.This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover.By this mean,the particles can explore a broad search space when utilising the additional searching experiences of other tasks.In addition,to enhance the performance on problems with different complementarity,they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback.They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems.Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.展开更多
Computational fluid dynamics(CFD) simulation is an effective approach to develop and optimise gas drainage design for underground longwall coal mining. As part of the project supported by the Australian Government Coa...Computational fluid dynamics(CFD) simulation is an effective approach to develop and optimise gas drainage design for underground longwall coal mining. As part of the project supported by the Australian Government Coal Mining Abatement Technology Support Package(CMATSP), threedimensional CFD simulations were conducted to test and optimise a conceptual design which proposes using horizontal boreholes to replace vertical boreholes at an underground coal mine in Australia.Drainage performance between a vertical borehole and a horizontal borehole was first carried out to compare their capacity and effectiveness. Then a series of cases with different horizontal borehole designs were simulated to optimise borehole configuration parameters such as location, diameter, and number of boreholes. The study shows that the horizontal borehole is able to create low pressure sinks that protect the workings from goaf gas ingresses by changing goaf gas flow directions, and that it has the advantage to continuously maintain such low pressure sinks near the tailgate as the longwall advances. An example of optimising horizontal borehole locations in the longwall lateral direction is also given in this paper.展开更多
The study of training hyperparameters optimisation problems remains underexplored in skin lesion research.This is the first report of using hierarchical optimisation to improve computational effort in a four-dimension...The study of training hyperparameters optimisation problems remains underexplored in skin lesion research.This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem.The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics.In the authors'demonstration,pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters,namely epoch,mini-batch value,initial learning rate,and gradient threshold.The iterative search of the optimal global-local solution is by using the derivative-based method.The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters.The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability.The authors'results showed that a small fraction of combinations(5%)from constrained global search space,in contrarily to 82%at the local level,can converge with early stopping conditions.The mean(standard deviation,SD)validation accuracies increased from 78.4(4.44)%to 82.9(1.8)%using the authors'system.The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy,precision,sensitivity,and specificity of 85.3%,75.6%,64.4%,and 97.2%,respectively.The authors'system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors'dataset.The extended experiments also showed its superior performance consistency across different deep networks,where the overall classification accuracy increased by 5%with this technique.This approach reduces the risk of search being trapped in a suboptimal solution,and its use may be expanded to network architecture optimisation for enhanced diagnostic performance.展开更多
We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide ...We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.展开更多
The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity problems can be addressed through improved water productivity and the options usually presumed i...The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity problems can be addressed through improved water productivity and the options usually presumed in this context are efficient water use and conversion of surface irrigation to pressurised systems. By replacing furrow irrigation with drip or centre pivot systems, the water efficiency can be improved by up to 30% to 45%. However, the installation and application of pumps and pipes, and the associated fuels needed for these alternatives increase energy consumption. A balance between the improvement in water use and the potential increase in energy consumption is required. When surface water is used, pressurised irrigation systems increase energy consumption substantially, by between 65% to 75%, and produce greenhouse gas emissions around 1.75 times higher than that of gravity based irrigation systems so their use should be carefully planned keeping in view adverse impact of carbon emissions on the environment and threat of increasing energy prices. With gravity-fed surface irrigation methods, the energy consumption is assumed to be negligible. This study has shown that a novel real-time infiltration model REIP has enabled implementation of real-time optimisation and gravity fed surface irrigation with real-time optimisation has potential to bring significant improvements in irrigation performance along with substantial water savings of 2.92 ML/ha which is equivalent to that given by pressurised systems. The real-time optimisation and control thus offers a modern, environment friendly and water efficient system with close to zero increase in energy consumption and minimal greenhouse gas emissions.展开更多
This paper presents a state of the art review of water quality optimisation models and techniques from early 1970s to date in terms of the model/technique category, model/technique type, purpose and application. The m...This paper presents a state of the art review of water quality optimisation models and techniques from early 1970s to date in terms of the model/technique category, model/technique type, purpose and application. The models are categorised into Mathematical Programming Models and Meta-heuristic Programming Models. Similarly, the techniques are categorised into Mathematical Programming Techniques and Meta-heuristic Programming Techniques. The review is concluded by drawing attention to the rare nature of application of interior-point methods to water quality optimisation.展开更多
The solution we propose optimizes the energy inside the wireless sensor network (WSN) with higher performance. The WSN is composed of many sensors nodes which collect the information, treat that information then send ...The solution we propose optimizes the energy inside the wireless sensor network (WSN) with higher performance. The WSN is composed of many sensors nodes which collect the information, treat that information then send it to the base station. The information is received by the base station (BS) then data?are?sent to the users by that BS. The most important element in sensor node is energy, as the lifetime of wireless sensor network depends on the sensor node energy. So many researches had been made in order to improve this energy basing routing protocols. As a result, we are able to propose a solution that optimizes this energy. In this paper, we are presenting a new approach of selecting node sensor base on routing protocol and process to send data to the base station. This ameliorates wireless sensor network lifetime and increases?the transmission sensor node to base station.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R66),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.
基金funding for this work from NSF-CMMI 2009270 and EPSRC EP/V034391/1.
文摘The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mass is subjected to a zero-mean Gaussian white noise excitation,the main objective of this study is to maximise the efficiency of the targeted energy transfer in the system.A surrogate optimisation algorithm is proposed for this purpose and adopted for the stochastic framework.The optimisations are conducted separately for the nonlinear stiffness coefficient alone as well as for both the nonlinear stiffness and damping coefficients together.Three different optimisation cost functions,based on either energy of the system’s components or the dissipated energy,are considered.The results demonstrate some clear trends in values of the nonlinear energy sink coefficients and show the effect of different cost functions on the optimal values of the nonlinear system’s coefficients.
基金National Natural Science Foundation of China,Grant/Award Number:51677059。
文摘Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.
文摘There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.
基金The project is funded by the Ministry of Higher Education Malaysia,under the Fundamental Research Grant Scheme(FRGS Grant No.FRGS/1/2017/TK07/SEGI/02/1).
文摘Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.
基金partially supported by YUTP-FRG funded by PETRONAS
文摘This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body with three degrees-of-freedom and its motions are analysed in time-domain using the implicit Newmark Beta technique. The mooring restoring force-excursion relationship is evaluated using quasi-static approach. MATLAB codes DATSpar and QSAML, are developed to compute the dynamic responses of truss spar platform and to determine the mooring system stiffness. To eliminate the conventional trial and error approach in the mooring system design, a numerical tool is also developed and described in this paper for optimising the mooring configuration. It has a graphical user interface and includes regrouping particle swarm optimisation technique combined with DATSpar and QSAML. A case study of truss spar platform with ten mooring lines is analysed using this numerical tool. The results show that optimum mooring system design benefits the oil and gas industry to economise the project cost in terms of material, weight, structural load onto the platform as well as manpower requirements. This tool is useful especially for the preliminary design of truss spar platforms and its mooring system.
文摘Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.
基金National Natural Science Foundation of China under Grant Nos.51675525 and 11725211.
文摘A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.
基金the management of Bharat Heavy Electricals Ltd., for funding this research programme
文摘Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubing, etc., in ultra-super critical and advanced ultra-super critical boiler applications. In the present work, laser welding process has been optimised for P92 material by using Taguchi based grey relational analysis(GRA).Bead on plate(BOP) trials were carried out using a 3.5 k W diffusion cooled slab CO_2 laser by varying laser power, welding speed and focal position. The optimum parameters have been derived by considering the responses such as depth of penetration, weld width and heat affected zone(HAZ) width. Analysis of variance(ANOVA) has been used to analyse the effect of different parameters on the responses. Based on ANOVA, laser power of 3 k W, welding speed of 1 m/min and focal plane at-4 mm have evolved as optimised set of parameters. The responses of the optimised parameters obtained using the GRA have been verified experimentally and found to closely correlate with the predicted value.? 2016 China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.
文摘Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.
文摘Optimisation of effective design parameters to reduce tooth bending stress for an automotive transmission gearbox is presented. A systematic investigation of effective design parameters for optimum design of a five-speed gearbox is studied. For this aim contact ratio effect on tooth bending stress by the changing of contact ratio with respect to pressure angle is analysed. Additionally, profile modification effects on tooth bending stress are presented. During the optimisation, the tooth bending stress is considered as the objective function, and all the geometric design parameters such as module, teeth number etc. are optimised under two different constraints, including tooth contact stress and constant gear centre distance. It can be concluded that higher the contact ratio results in a reduced tooth bending stress, while higher the pressure angle caused an increase in tooth bending stress and contact stress, since decreases in the contact ratio. In addition, application of positive profile modification on tooth reduces tooth bending stress. All of the obtained optimum solutions satisfy all constraints.
文摘Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task particles.Meanwhile,these algorithms use a fixed inter-task learning probability throughout the evolution process.However,the parameter is problem dependent and can be various at different stages of the evolution.In this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO.This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover.By this mean,the particles can explore a broad search space when utilising the additional searching experiences of other tasks.In addition,to enhance the performance on problems with different complementarity,they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback.They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems.Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.
基金the Department of Industry and Science,Australian Government for funding this researchthe management and staff of Glencore Bulga Underground Operations for their significant contributions in this project
文摘Computational fluid dynamics(CFD) simulation is an effective approach to develop and optimise gas drainage design for underground longwall coal mining. As part of the project supported by the Australian Government Coal Mining Abatement Technology Support Package(CMATSP), threedimensional CFD simulations were conducted to test and optimise a conceptual design which proposes using horizontal boreholes to replace vertical boreholes at an underground coal mine in Australia.Drainage performance between a vertical borehole and a horizontal borehole was first carried out to compare their capacity and effectiveness. Then a series of cases with different horizontal borehole designs were simulated to optimise borehole configuration parameters such as location, diameter, and number of boreholes. The study shows that the horizontal borehole is able to create low pressure sinks that protect the workings from goaf gas ingresses by changing goaf gas flow directions, and that it has the advantage to continuously maintain such low pressure sinks near the tailgate as the longwall advances. An example of optimising horizontal borehole locations in the longwall lateral direction is also given in this paper.
基金Number:FRGS/1/2020/TK0/UTHM/02/27Universiti Tun Hussein Onn Malaysia,Grant/Award Number:H766。
文摘The study of training hyperparameters optimisation problems remains underexplored in skin lesion research.This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem.The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics.In the authors'demonstration,pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters,namely epoch,mini-batch value,initial learning rate,and gradient threshold.The iterative search of the optimal global-local solution is by using the derivative-based method.The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters.The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability.The authors'results showed that a small fraction of combinations(5%)from constrained global search space,in contrarily to 82%at the local level,can converge with early stopping conditions.The mean(standard deviation,SD)validation accuracies increased from 78.4(4.44)%to 82.9(1.8)%using the authors'system.The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy,precision,sensitivity,and specificity of 85.3%,75.6%,64.4%,and 97.2%,respectively.The authors'system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors'dataset.The extended experiments also showed its superior performance consistency across different deep networks,where the overall classification accuracy increased by 5%with this technique.This approach reduces the risk of search being trapped in a suboptimal solution,and its use may be expanded to network architecture optimisation for enhanced diagnostic performance.
文摘We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.
文摘The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity problems can be addressed through improved water productivity and the options usually presumed in this context are efficient water use and conversion of surface irrigation to pressurised systems. By replacing furrow irrigation with drip or centre pivot systems, the water efficiency can be improved by up to 30% to 45%. However, the installation and application of pumps and pipes, and the associated fuels needed for these alternatives increase energy consumption. A balance between the improvement in water use and the potential increase in energy consumption is required. When surface water is used, pressurised irrigation systems increase energy consumption substantially, by between 65% to 75%, and produce greenhouse gas emissions around 1.75 times higher than that of gravity based irrigation systems so their use should be carefully planned keeping in view adverse impact of carbon emissions on the environment and threat of increasing energy prices. With gravity-fed surface irrigation methods, the energy consumption is assumed to be negligible. This study has shown that a novel real-time infiltration model REIP has enabled implementation of real-time optimisation and gravity fed surface irrigation with real-time optimisation has potential to bring significant improvements in irrigation performance along with substantial water savings of 2.92 ML/ha which is equivalent to that given by pressurised systems. The real-time optimisation and control thus offers a modern, environment friendly and water efficient system with close to zero increase in energy consumption and minimal greenhouse gas emissions.
文摘This paper presents a state of the art review of water quality optimisation models and techniques from early 1970s to date in terms of the model/technique category, model/technique type, purpose and application. The models are categorised into Mathematical Programming Models and Meta-heuristic Programming Models. Similarly, the techniques are categorised into Mathematical Programming Techniques and Meta-heuristic Programming Techniques. The review is concluded by drawing attention to the rare nature of application of interior-point methods to water quality optimisation.
文摘The solution we propose optimizes the energy inside the wireless sensor network (WSN) with higher performance. The WSN is composed of many sensors nodes which collect the information, treat that information then send it to the base station. The information is received by the base station (BS) then data?are?sent to the users by that BS. The most important element in sensor node is energy, as the lifetime of wireless sensor network depends on the sensor node energy. So many researches had been made in order to improve this energy basing routing protocols. As a result, we are able to propose a solution that optimizes this energy. In this paper, we are presenting a new approach of selecting node sensor base on routing protocol and process to send data to the base station. This ameliorates wireless sensor network lifetime and increases?the transmission sensor node to base station.
