This paper considers the formation control problem for a group of unmanned aerial vehicles( UAVs)employing consensus with different optimizers. A group of UAVs can never accomplish difficult tasks without formation be...This paper considers the formation control problem for a group of unmanned aerial vehicles( UAVs)employing consensus with different optimizers. A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load. Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity. However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus. It is natural to modify this method with lots of optimizers. In order to reduce overshoot and smooth trajectories, this paper first adopted particle swarm optimization( PSO), then pigeon-inspired optimization( PIO) to modify the consensus. PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation. As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy( SD-PIO) is proposed. Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.展开更多
In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during th...In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.展开更多
Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-f...Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.展开更多
Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss...Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.展开更多
In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorith...In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.展开更多
Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybri...Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-展开更多
This paper models the Mars UAV formation exploring the surface of Mars,and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs.Based on their specialty,constr...This paper models the Mars UAV formation exploring the surface of Mars,and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs.Based on their specialty,constrained Delaunay triangulation,Yen-K shortest path algorithm,the collaborative function,and the improved pigeon-inspired optimization(PIO)algorithm are integrated to solve the obstacle avoidance for the formation.Since the steering maneuver costs much energy and increases instabilities vulnerable in extraterrestrial exploration,the paper focuses on the route smoothness problem.The PIO is improved to be suitable for smooth routes and is compatible with other PIO variants.The simulation results show that the sum of the steering angle,namely the performance index,is e®ectively reduced and satises the obstacle avoidance requirements for Mars UAV formation.展开更多
This paper presents a novel multiple unmanned aerial vehicde(UAV)swarm cotoller based on the fractional alculus theory.This controller i designed baed on fractional order Darwinian pigeon-inepired optimization(F 0DPI0...This paper presents a novel multiple unmanned aerial vehicde(UAV)swarm cotoller based on the fractional alculus theory.This controller i designed baed on fractional order Darwinian pigeon-inepired optimization(F 0DPI0)and PID algorithm.Several comparative simulations are conducted in the paper.The simulation results reveal that FODPIObased muli-UAV formation controller is superior to the basic PIO and dilTerential evolution(DE)method.The fractional oelfcdent in F ODPIO algorithm makes it eflective optimbation with fast convergence rate,small oversboot,and better stability.Therefore,the contnoller propoeed in this paper is fessible and robust.展开更多
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Ex...With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.展开更多
Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requiremen...Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requirements are encountered.This paper establishes a decision-making framework for multiple unmanned aerial vehicles(multi-UAV)based on the well-known pigeon-inspired optimization(PIO)algorithm.By addressing the problem from a hierarchical structural perspective,the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements,utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV.In the second stage,building upon the decisions made in the preceding stage,each UAV is abstracted as an agent maximizing its own task execution benefits.An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed,enabling the acquisition of the optimal task execution sequence.Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology.Moreover,dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.展开更多
The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle(UAV) security will become on...The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle(UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference,high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target(MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation(TIS) method based on an improved pigeon-inspired optimization(PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search(HCS) and vertical crossover search(VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur's entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary,this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.展开更多
The aerial manipulator expands the scope of unmanned aerial vehicle(UAV)'s application as well as increases the di±culties in the design of the controller.To better control the aerial manipulator for di®...The aerial manipulator expands the scope of unmanned aerial vehicle(UAV)'s application as well as increases the di±culties in the design of the controller.To better control the aerial manipulator for di®erent trajectories tracking under di®erent conditions,a new dual-layer controller is designed in this paper.The integral backstepping sliding mode controller(IBSMC)is applied to the outer-loop controller and backstepping controller(BC)is applied to the innerloop controller.To improve the performance of the system,an improved pigeon-inspired optimization(PIO)algorithm called group coevolution and immigration pigeon-inspired optimization(GCIPIO)algorithm is proposed to optimize the controller parameters of IBSMC.GCIPIO algorithm utilizes the group coevolution and immigration mechanisms.A series of simulations are conducted to show the advantage of the proposed method.The results illustrate that the proposed method ensures the closed-loop system has less end-e®ector tracking error.展开更多
基金Natural Science Foundation of China under Grant(61333004)
文摘This paper considers the formation control problem for a group of unmanned aerial vehicles( UAVs)employing consensus with different optimizers. A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load. Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity. However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus. It is natural to modify this method with lots of optimizers. In order to reduce overshoot and smooth trajectories, this paper first adopted particle swarm optimization( PSO), then pigeon-inspired optimization( PIO) to modify the consensus. PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation. As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy( SD-PIO) is proposed. Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.
基金supported by Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelli-gence",China(No.2018AAA0100803)National Natural Science Foundation of China(Nos.U20B2071,91948204,U1913602)Aeronautical Foundation of China(No.20185851022).
文摘In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.
基金supported in part by National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057)+1 种基金National Natural Science Foundation of China(61876070)Jilin Province Science and Technology Development Plan Project(20190303134SF).
文摘Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.
基金supported by the Fundamental Research Funds for the Central Scientific Research Institutes (Grant No. 20200306)。
文摘Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified.
基金supported by the National Natural Science Foundation of China(Grant Nos.61425008,61333004&61273054)Aeronautical Foundation of China(Grant No.2015ZA51013)
文摘In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.
基金supported by the Natural Science Foundation of Hubei Province(Grant No.2015CFB586)
文摘Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-
文摘This paper models the Mars UAV formation exploring the surface of Mars,and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs.Based on their specialty,constrained Delaunay triangulation,Yen-K shortest path algorithm,the collaborative function,and the improved pigeon-inspired optimization(PIO)algorithm are integrated to solve the obstacle avoidance for the formation.Since the steering maneuver costs much energy and increases instabilities vulnerable in extraterrestrial exploration,the paper focuses on the route smoothness problem.The PIO is improved to be suitable for smooth routes and is compatible with other PIO variants.The simulation results show that the sum of the steering angle,namely the performance index,is e®ectively reduced and satises the obstacle avoidance requirements for Mars UAV formation.
基金supported by Science and Technology Innovation 2030-Key Project of“New Generation A rtificial Intelligence”under grant#2018A AA0102403National Natural Science Foundation of China under grant#U20B2071,#91948204,#U1913602 and#U19B2033.
文摘This paper presents a novel multiple unmanned aerial vehicde(UAV)swarm cotoller based on the fractional alculus theory.This controller i designed baed on fractional order Darwinian pigeon-inepired optimization(F 0DPI0)and PID algorithm.Several comparative simulations are conducted in the paper.The simulation results reveal that FODPIObased muli-UAV formation controller is superior to the basic PIO and dilTerential evolution(DE)method.The fractional oelfcdent in F ODPIO algorithm makes it eflective optimbation with fast convergence rate,small oversboot,and better stability.Therefore,the contnoller propoeed in this paper is fessible and robust.
基金This work was supported by the National Key Research and Development Program of China(No.2018YFC1604000)the National Natural Science Foundation of China(Nos.61806138,61772478,U1636220,61961160707,and 61976212)+2 种基金the Key R&D Program of Shanxi Province(High Technology)(No.201903D121119)the Key R&D Program of Shanxi Province(International Cooperation)(No.201903D421048)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province,China(No.201903D421003).
文摘With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.
文摘Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requirements are encountered.This paper establishes a decision-making framework for multiple unmanned aerial vehicles(multi-UAV)based on the well-known pigeon-inspired optimization(PIO)algorithm.By addressing the problem from a hierarchical structural perspective,the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements,utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV.In the second stage,building upon the decisions made in the preceding stage,each UAV is abstracted as an agent maximizing its own task execution benefits.An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed,enabling the acquisition of the optimal task execution sequence.Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology.Moreover,dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.
文摘The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle(UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference,high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target(MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation(TIS) method based on an improved pigeon-inspired optimization(PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search(HCS) and vertical crossover search(VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur's entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary,this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.
基金the Science and Technology Innovation 2030-Key Project of\New Generation Articial Intelligence"under grant#2018AAA0102403National Natural Science Foundation of China under grant#U20B2071,#91948204,#T2121003,#U1913602 and#U19B2033.
文摘The aerial manipulator expands the scope of unmanned aerial vehicle(UAV)'s application as well as increases the di±culties in the design of the controller.To better control the aerial manipulator for di®erent trajectories tracking under di®erent conditions,a new dual-layer controller is designed in this paper.The integral backstepping sliding mode controller(IBSMC)is applied to the outer-loop controller and backstepping controller(BC)is applied to the innerloop controller.To improve the performance of the system,an improved pigeon-inspired optimization(PIO)algorithm called group coevolution and immigration pigeon-inspired optimization(GCIPIO)algorithm is proposed to optimize the controller parameters of IBSMC.GCIPIO algorithm utilizes the group coevolution and immigration mechanisms.A series of simulations are conducted to show the advantage of the proposed method.The results illustrate that the proposed method ensures the closed-loop system has less end-e®ector tracking error.
