The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DA...The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.展开更多
The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter stra...The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.展开更多
This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment ...This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.展开更多
In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constrai...In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.展开更多
In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramou...In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones,...A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.展开更多
We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to es...We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.展开更多
Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In...Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.展开更多
Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much at...Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.展开更多
Photovoltaic(PV)systems are adversely affected by partial shading and non-uniform conditions.Meanwhile,the addition of a bypass shunt diode to each PV module prevents hotspots.It also produces numerous peaks in the PV...Photovoltaic(PV)systems are adversely affected by partial shading and non-uniform conditions.Meanwhile,the addition of a bypass shunt diode to each PV module prevents hotspots.It also produces numerous peaks in the PV array’s power-voltage characteristics,thereby trapping conventional maximum power point tracking(MPPT)methods in local peaks.Swarm optimization approaches can be used to address this issue.However,these strategies have an unreasonably long convergence time.The Grey Wolf Optimizer(GWO)is a fast and more dependable optimization algorithm.This renders it a good option for MPPT of PV systems operating in varying partial shading.The conventional GWO method involves a long conversion time,large steady-state oscillations,and a high failure rate.This work attempts to address these issues by combining Cuckoo Search(CS)with the GWO algorithm to improve the MPPT performance.The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe(P&O).A comparative analysis reveals that under non-uniform operating conditions,the hybrid GWO CS(GWOCS)approach presented in this article outperforms the GWO and P&O approaches.展开更多
Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination betwe...Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future.展开更多
The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo searc...The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.展开更多
The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the code...The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.展开更多
In this paper, the principle of Cuckoo algorithm is introduced, and the traditional Cuckoo algorithm is improved to establish a mathematical model of multi-objective optimization scheduling. Based on the improved algo...In this paper, the principle of Cuckoo algorithm is introduced, and the traditional Cuckoo algorithm is improved to establish a mathematical model of multi-objective optimization scheduling. Based on the improved algorithm, the model is optimized to a certain extent. Through analysis, it is proved that the improved algorithm has higher computational accuracy and can effectively improve the global convergence.展开更多
Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibi...Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms.This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm(NAGCSA)to address the scheduling issue in cloud computing.Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation.The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient,while the global search is performed by using levy flights.The amalgamation of NAG and CSA helps in cost reduction and time-saving for users.The simulation has been carried out on the CloudSim tool on three different real datasets;NASA,HPC2N,and SDSC.The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms(GA,PSO,and CSA),and statistical significance is carried on mean,standard deviation,and best for each algorithm.It has been established that the proposed algorithm minimizes the execution cost and makespan,hence enhancing the quality of service for users.展开更多
The lifetime data of products with multiple failure modes which are collected from life testing are often fitted by the mixed Weibull distributions. Since the mixed Weibull distributions contain no less than five para...The lifetime data of products with multiple failure modes which are collected from life testing are often fitted by the mixed Weibull distributions. Since the mixed Weibull distributions contain no less than five parameters,the parameter estimation is difficult and inaccurate. In order to enhance the accuracy,a new method of parameter estimation based on Cuckoo search( CS) is proposed. An optimization model for the mixed Weibull distribution is formulated by minimizing the residual sum of squares. The optimal parameters are searched via CS algorithm. In the case study,the lifetime data come from the life testing of diesel injectors and are fitted by the twocomponent Weibull mixture. Regarding the maximum absolute error and the accumulative absolute error between estimated and observed values as the accuracy index of parameter estimation,the results of four parameter estimation methods that the graphic estimation method,the nonlinear least square method,the optimization method based on particle swarm optimization( PSO) and the proposed method are compared. The result shows that the proposed method is more efficient and more accurate than the other three methods.展开更多
Cuckoo search(CS) has been used successfully for solving global optimization problems.From a theoretical point of view,the convergence of the CS is an important issue.In this paper,convergence analysis of CS was studi...Cuckoo search(CS) has been used successfully for solving global optimization problems.From a theoretical point of view,the convergence of the CS is an important issue.In this paper,convergence analysis of CS was studied.The transition probability characteristics of the population to construct a Markov chain were analyzed.The homogeneity of the Markov chain was derived based on stochastic process theory.Then it was proved to be an absorbing state Markov chain.Consequently,the global convergence of CS was deduced based on conditions of convergence sequence and total probability formula,and the expected convergence time was given.Finally,a series of experiments were conducted.Experimental results were analyzed and it is observed that CS seems to perform better than PSO.展开更多
The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificia...The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.展开更多
Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massiv...Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search （MRMCS）, an efficient modified cuckoo search （MCS） implementation on a MapReduce architecture--Hadoop. MapReduce particle swarm optimization （MRPSO） from a previous work is also implemented for comparison. Four evaluation functions and two engineering design problems are used to conduct experiments. As a result, MRMCS shows better convergence in obtaining optimality than MRPSO with two to four times speed-up.展开更多
文摘The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.
基金funded by the NationalKey Research and Development Program of China under Grant No.11974373.
文摘The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.
基金supported by the National Natural Science Foundation of China(61971470).
文摘This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.
基金Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPSAU-2021/01/17793)rch work through the project number (IFPSAU-2021/01/17793)。
文摘In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.
文摘In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
基金supported in part by the National Key Research and Development Program of China(2017YFB0306400)in part by the National Natural Science Foundation of China(61573089,71472080,71301066)Liaoning Province Dr.Research Foundation of China(20175032)
文摘A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 60473042,60573067 and 60803102)
文摘We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.
基金supported by the National Key Research and Development Program of China [grant number2017YFA0604500]
文摘Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.
基金Projects([2013]2082,[2009]2061)supported by the Science Technology Foundation of Guizhou Province,ChinaProject([2013]140)supported by the Excellent Science Technology Innovation Talents in Universities of Guizhou Province,ChinaProject(2008040)supported by the Natural Science Research in Education Department of Guizhou Province,China
文摘Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.
文摘Photovoltaic(PV)systems are adversely affected by partial shading and non-uniform conditions.Meanwhile,the addition of a bypass shunt diode to each PV module prevents hotspots.It also produces numerous peaks in the PV array’s power-voltage characteristics,thereby trapping conventional maximum power point tracking(MPPT)methods in local peaks.Swarm optimization approaches can be used to address this issue.However,these strategies have an unreasonably long convergence time.The Grey Wolf Optimizer(GWO)is a fast and more dependable optimization algorithm.This renders it a good option for MPPT of PV systems operating in varying partial shading.The conventional GWO method involves a long conversion time,large steady-state oscillations,and a high failure rate.This work attempts to address these issues by combining Cuckoo Search(CS)with the GWO algorithm to improve the MPPT performance.The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe(P&O).A comparative analysis reveals that under non-uniform operating conditions,the hybrid GWO CS(GWOCS)approach presented in this article outperforms the GWO and P&O approaches.
基金supported by the Natural Science Foundation of Chongqing,China(No.cstc2020jcyj-msxmX0836)the Fundamental Research Funds for the Central Universities(No.2020CDJ-LHZZ-002)the National Natural Science Foundation of China(No.52074041).
文摘Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future.
基金supported by the National Natural Science Foundation of China(61273083 and 61374012)
文摘The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.
文摘The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.
文摘In this paper, the principle of Cuckoo algorithm is introduced, and the traditional Cuckoo algorithm is improved to establish a mathematical model of multi-objective optimization scheduling. Based on the improved algorithm, the model is optimized to a certain extent. Through analysis, it is proved that the improved algorithm has higher computational accuracy and can effectively improve the global convergence.
基金This research work is funded by University Grant Commission under National Fellowship Programme,Grant No.2017-18/29146.
文摘Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms.This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm(NAGCSA)to address the scheduling issue in cloud computing.Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation.The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient,while the global search is performed by using levy flights.The amalgamation of NAG and CSA helps in cost reduction and time-saving for users.The simulation has been carried out on the CloudSim tool on three different real datasets;NASA,HPC2N,and SDSC.The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms(GA,PSO,and CSA),and statistical significance is carried on mean,standard deviation,and best for each algorithm.It has been established that the proposed algorithm minimizes the execution cost and makespan,hence enhancing the quality of service for users.
文摘The lifetime data of products with multiple failure modes which are collected from life testing are often fitted by the mixed Weibull distributions. Since the mixed Weibull distributions contain no less than five parameters,the parameter estimation is difficult and inaccurate. In order to enhance the accuracy,a new method of parameter estimation based on Cuckoo search( CS) is proposed. An optimization model for the mixed Weibull distribution is formulated by minimizing the residual sum of squares. The optimal parameters are searched via CS algorithm. In the case study,the lifetime data come from the life testing of diesel injectors and are fitted by the twocomponent Weibull mixture. Regarding the maximum absolute error and the accumulative absolute error between estimated and observed values as the accuracy index of parameter estimation,the results of four parameter estimation methods that the graphic estimation method,the nonlinear least square method,the optimization method based on particle swarm optimization( PSO) and the proposed method are compared. The result shows that the proposed method is more efficient and more accurate than the other three methods.
基金National Natural Science Foundation of China(No.61174065)
文摘Cuckoo search(CS) has been used successfully for solving global optimization problems.From a theoretical point of view,the convergence of the CS is an important issue.In this paper,convergence analysis of CS was studied.The transition probability characteristics of the population to construct a Markov chain were analyzed.The homogeneity of the Markov chain was derived based on stochastic process theory.Then it was proved to be an absorbing state Markov chain.Consequently,the global convergence of CS was deduced based on conditions of convergence sequence and total probability formula,and the expected convergence time was given.Finally,a series of experiments were conducted.Experimental results were analyzed and it is observed that CS seems to perform better than PSO.
文摘The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.
文摘Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search （MRMCS）, an efficient modified cuckoo search （MCS） implementation on a MapReduce architecture--Hadoop. MapReduce particle swarm optimization （MRPSO） from a previous work is also implemented for comparison. Four evaluation functions and two engineering design problems are used to conduct experiments. As a result, MRMCS shows better convergence in obtaining optimality than MRPSO with two to four times speed-up.
