Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality ...Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions.While heuristic optimization algorithms vary in their specific details,they often exhibit common patterns that are essential to their effectiveness.This paper aims to analyze and explore common patterns in heuristic optimization algorithms.Through a comprehensive review of the literature,we identify the patterns that are commonly observed in these algorithms,including initialization,local search,diversity maintenance,adaptation,and stochasticity.For each pattern,we describe the motivation behind it,its implementation,and its impact on the search process.To demonstrate the utility of our analysis,we identify these patterns in multiple heuristic optimization algorithms.For each case study,we analyze how the patterns are implemented in the algorithm and how they contribute to its performance.Through these case studies,we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms.Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness.By understanding and incorporating these patterns into the design of new algorithms,researchers can develop more efficient and effective optimization algorithms.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorit...Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of OPF.This paper provides an overview of the latest applications of advanced HOAs in OPF problems.The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.展开更多
Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity...Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach（HHA） based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle（ASLV）.A non-learning random function（NLRF） is proposed to con-trol low-level meta-heuristics（LLMHs） that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.展开更多
Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency wit...Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency with minimum possible sliver. In this arti- cle, a methodology has been presented for designing three-dimensional （3D） grain configuration of radial slot for upper stage solid rocket motors. The design process involves parametric modeling of the geometry in computer aided design （CAD） software through dynamic variables that define the complex configuration. Grain bum back is achieved by making new surfaces at each web increment and calculating geometrical properties at each step. Geometrical calculations are based on volume and change-in-volume calculations. Equilibrium pressure method is used to calculate the internal ballistics. Genetic algorithm （GA） has been used as the optimizer because of its robustness and efficient capacity to explore the design space for global optimum solution and eliminate the requirement of an initial guess. Average thrust maximization under design constraints is the objective function.展开更多
Internet of Things is an ecosystem of interconnected devices that are accessible through the internet.The recent research focuses on adding more smartness and intelligence to these edge devices.This makes them suscept...Internet of Things is an ecosystem of interconnected devices that are accessible through the internet.The recent research focuses on adding more smartness and intelligence to these edge devices.This makes them susceptible to various kinds of security threats.These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field.In this regard,block cipher has been one of the most reliable options through which data security is accomplished.The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes.For the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these techniques.On the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely non-deterministic.In this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based S-boxes.Secondly,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of S-boxes.Based on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of s-boxes.The results of our method validate that the proposed dynamic S-boxes are effective for the block ciphers.Moreover,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.展开更多
Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applic...Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers(CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine(VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm(HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.展开更多
Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first fo...Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.展开更多
Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One ...Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.展开更多
The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling c...The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.展开更多
Beam shaping in nanophotonic systems remains a challenge due to the reliance on complex heuristic optimization procedures.In this work,we experimentally demonstrate a novel approach to topological beam shaping using J...Beam shaping in nanophotonic systems remains a challenge due to the reliance on complex heuristic optimization procedures.In this work,we experimentally demonstrate a novel approach to topological beam shaping using Jackiw-Rebbi states in metasurfaces.By fabricating thin-film dielectric structures with engineered Dirac-mass distributions,we create domain walls that allow precise control over beam profiles.We observe the emergence of Jackiw-Rebbi states and confirm their localized characteristics.Notably,we achieve a flat-top beam profile by carefully tailoring the Diracmass distribution,highlighting the potential of this method for customized beam shaping.This experimental realization establishes our approach as a new mechanism for beam control,rooted in topological physics,and offers an efficient strategy for nanophotonic design.展开更多
As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mis...As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.展开更多
The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environme...The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.展开更多
Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the...Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the available fuel alternatives for each unit while satisfying various constraints in power systems. In this paper, a Fuzzified Squirrel Search Algorithm (FSSA) algorithm is proposed to solve the single-area multi-fuel economic dispatch (SAMFED) and MAMFED problems. Squirrel Search Algorithm (SSA) mimics the foraging behavior of squirrels based on the dynamic jumping and gliding strategies. In the SSA approach, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The suggested approach considers the line losses, valve point loading impacts, multi-fuel alternatives, and tie-line limits of the power system. Because of the contradicting nature of fuel cost and pollutant emission objectives, weighted sum approach and price penalty factor are used to transfer the bi-objective function into a single objective function. Furthermore, a fuzzy decision strategy is introduced to find one of the Pareto optimal fronts as the best compromised solution. The feasibility of the FSSA is tested on a three-area test system for both the SAMFED and MAMFED problems. The results of FSSA approach are compared with other heuristic approaches in the literature. Multi-objective performance indicators such as generational distance, spacing metric and ratio of non-dominated individuals are evaluated to validate the effectiveness of FSSA. The results divulge that the FSSA is a promising approach to solve the SAMFED and MAMFED problems while providing a better compromise solution in comparison with other heuristic approaches.展开更多
This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management ...This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.展开更多
Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccurac...Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.展开更多
This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whal...This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers.展开更多
文摘Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions.While heuristic optimization algorithms vary in their specific details,they often exhibit common patterns that are essential to their effectiveness.This paper aims to analyze and explore common patterns in heuristic optimization algorithms.Through a comprehensive review of the literature,we identify the patterns that are commonly observed in these algorithms,including initialization,local search,diversity maintenance,adaptation,and stochasticity.For each pattern,we describe the motivation behind it,its implementation,and its impact on the search process.To demonstrate the utility of our analysis,we identify these patterns in multiple heuristic optimization algorithms.For each case study,we analyze how the patterns are implemented in the algorithm and how they contribute to its performance.Through these case studies,we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms.Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness.By understanding and incorporating these patterns into the design of new algorithms,researchers can develop more efficient and effective optimization algorithms.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
基金This work was partially supported by Hong Kong RGC Theme Based Research Scheme Grants No.T23-407/13 N and T23-701/14 N.
文摘Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of OPF.This paper provides an overview of the latest applications of advanced HOAs in OPF problems.The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.
文摘Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach（HHA） based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle（ASLV）.A non-learning random function（NLRF） is proposed to con-trol low-level meta-heuristics（LLMHs） that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.
文摘Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency with minimum possible sliver. In this arti- cle, a methodology has been presented for designing three-dimensional （3D） grain configuration of radial slot for upper stage solid rocket motors. The design process involves parametric modeling of the geometry in computer aided design （CAD） software through dynamic variables that define the complex configuration. Grain bum back is achieved by making new surfaces at each web increment and calculating geometrical properties at each step. Geometrical calculations are based on volume and change-in-volume calculations. Equilibrium pressure method is used to calculate the internal ballistics. Genetic algorithm （GA） has been used as the optimizer because of its robustness and efficient capacity to explore the design space for global optimum solution and eliminate the requirement of an initial guess. Average thrust maximization under design constraints is the objective function.
文摘Internet of Things is an ecosystem of interconnected devices that are accessible through the internet.The recent research focuses on adding more smartness and intelligence to these edge devices.This makes them susceptible to various kinds of security threats.These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field.In this regard,block cipher has been one of the most reliable options through which data security is accomplished.The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes.For the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these techniques.On the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely non-deterministic.In this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based S-boxes.Secondly,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of S-boxes.Based on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of s-boxes.The results of our method validate that the proposed dynamic S-boxes are effective for the block ciphers.Moreover,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.
文摘Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers(CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine(VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm(HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.
基金supported by the National Natural Science Foundation of China(71171197)the National Basic Research Program of China(973 Program)(613154)
文摘Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.
基金supported by the National Basic Research Program of China (973 Program) under Grant 2013CB329104the National Natural Science Foundation of China under Grant 61372124 and 61427801the Key Projects of Natural Science Foundation of Jiangsu University under Grant 11KJA510001
文摘Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.
文摘The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.
基金supported by the Leader Researcher Program(NRF-2019R1A3B2068083)The National Research Facilities and Equipment Center(NFEC)at the Ministry of Science and ICT Support from the supporting project for advancement of leading research facilities(PG2023003-03)the quantum computing technology development program of the Quantum Information Research Support Center,funded through the National research foundation of Korea(2020M3H3A1110365).
文摘Beam shaping in nanophotonic systems remains a challenge due to the reliance on complex heuristic optimization procedures.In this work,we experimentally demonstrate a novel approach to topological beam shaping using Jackiw-Rebbi states in metasurfaces.By fabricating thin-film dielectric structures with engineered Dirac-mass distributions,we create domain walls that allow precise control over beam profiles.We observe the emergence of Jackiw-Rebbi states and confirm their localized characteristics.Notably,we achieve a flat-top beam profile by carefully tailoring the Diracmass distribution,highlighting the potential of this method for customized beam shaping.This experimental realization establishes our approach as a new mechanism for beam control,rooted in topological physics,and offers an efficient strategy for nanophotonic design.
文摘As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.
基金supported via funding from Prince Sattam Bin Abdulaziz University(No.PSAU/2023/R/1444).
文摘The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.
文摘Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the available fuel alternatives for each unit while satisfying various constraints in power systems. In this paper, a Fuzzified Squirrel Search Algorithm (FSSA) algorithm is proposed to solve the single-area multi-fuel economic dispatch (SAMFED) and MAMFED problems. Squirrel Search Algorithm (SSA) mimics the foraging behavior of squirrels based on the dynamic jumping and gliding strategies. In the SSA approach, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The suggested approach considers the line losses, valve point loading impacts, multi-fuel alternatives, and tie-line limits of the power system. Because of the contradicting nature of fuel cost and pollutant emission objectives, weighted sum approach and price penalty factor are used to transfer the bi-objective function into a single objective function. Furthermore, a fuzzy decision strategy is introduced to find one of the Pareto optimal fronts as the best compromised solution. The feasibility of the FSSA is tested on a three-area test system for both the SAMFED and MAMFED problems. The results of FSSA approach are compared with other heuristic approaches in the literature. Multi-objective performance indicators such as generational distance, spacing metric and ratio of non-dominated individuals are evaluated to validate the effectiveness of FSSA. The results divulge that the FSSA is a promising approach to solve the SAMFED and MAMFED problems while providing a better compromise solution in comparison with other heuristic approaches.
文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.
基金supported by the National Natural Science Foundation of China(72101066,72131005,72121001,72171062,91846301,and 71772053)Heilongjiang Natural Science Excellent Youth Fund(YQ2022G004)Key Research and Development Projects of Heilongjiang Province(JD22A003).
文摘Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.
文摘This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers.
