Autonomous Maneuver Decisions via Transfer Learning Pigeon-Inspired Optimization for UCAVs in Dogfight Engagements

This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.

A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy

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.

Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

This paper proposes a comprehensive design scheme for the extremum seeking control(ESC)of the unmanned aerial vehicle(UAV)close formation flight.The proposed design scheme combines a Newton-Raphson method with an extended Kalman filter(EKF)to dynamically estimate the optimal position of the following UAV relative to the leading UAV.To reflect the wake vortex effects reliably,the drag coefficient induced by the wake vortex is considered as a performance function.Then,the performance function is parameterized by the first-order and second-order terms of its Taylor series expansion.Given the excellent performance of nonlinear estimation,the EKF is used to estimate the gradient and the Hessian matrix of the parameterized performance function.The output feedback of the proposed scheme is determined by iterative calculation of the Newton-Raphson method.Compared with the traditional ESC and the classic ESC,the proposed design scheme avoids the slow continuous time integration of the gradient.This allows a faster convergence of relative position extremum.Furthermore,the proposed method can provide a smoother command during the seeking process as the second-order term of the performance function is taken into account.The convergence analysis of the proposed design scheme is accomplished by showing that the output feedback is a supermartingale sequence.To improve estimation performance of the EKF,a improved pigeon-inspired optimization(IPIO)is proposed to automatically tune the noise covariance matrix.Monte Carlo simulations for a three-UAV close formation show that the proposed design scheme is robust to the initial position of the following UAV.

基于PIO-ADRC的自主水下航行器运动控制

为进一步提高自主水下航行器(Autonomous Underwater Vehicle,AUV)的控制精度,对六自由度全驱动AUV进行建模,并搭建相应的自抗扰控制器(Active Disturbance Rejection Controller,ADRC)。考虑到传统ADRC待调整参数过多,且必须随着系统进行相应的参数调整,针对此,提出基于鸽群算法优化(Pigeon-Inspired Optimization,PIO)的ADRC(PIO-ADRC),并在相同仿真条件下将其与经过PIO的比例-积分-微分(Pigeon-Inspired Optimized Proportional Integral Derivative,PIO-PID)控制器和传统ADRC的控制效果进行对比。结果表明,使用PIO的ADRC超调量小、稳态误差小、控制效率更高。

Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

Bilateral Collaborative Optimization for Cloud Manufacturing Service

Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.

Improved Pigeon-Inspired Optimization in an Integrated Obstacle Avoidance Method for Mars UAV Formation

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.

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

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.

基于CGAPIO的航天器编队重构路径规划方法

针对航天器编队重构的路径规划问题,考虑燃料消耗和碰撞概率等约束条件,以及基本鸽群算法存在的问题,提出一种基于混沌初始化和高斯扰动的自适应鸽群(CGAPIO)算法。为了得到多样性和覆盖性更好的鸽群初始值,采用Tent Map混沌模型进行鸽群初始化操作;在地图和指南针算子阶段,为提高全局搜索能力,引入了自适应的权重因子和学习因子更新个体的位置和速度;在地标算子阶段,为避免算法陷入局部最优,将高斯扰动加入到鸽群中心位置。仿真实验结果表明:CGAPIO算法与基本鸽群算法和粒子群算法相比,提高了全局搜索能力,避免了局部最优,规划得到的路径更加平滑,各航天器碰撞概率较低,编队重构消耗的总燃料至少减少了12%。

基于有限忍耐度鸽群优化的无人机近距空战机动决策

由于对抗双方态势的快速变化,无人机近距空战机动自主决策困难且复杂,是空中对抗的一个难点。对此,提出一种基于有限忍耐度鸽群优化(FTPIO)算法的无人机近距空战机动决策方法。该方法主要包括基于机动动作库的对手行动预测和基于FTPIO算法的机动控制量和执行时间优化求解两个部分。为提升基本鸽群优化(PIO)算法的全局探索能力,引入有限忍耐度策略,在鸽子个体几次迭代中没有找到更优解时对其属性进行一次重置,避免陷入局部最优陷阱。该方法采用的优化变量是无人机运动模型控制变量的增量,打破了机动库的限制。通过和极小极大方法、基本PIO算法和粒子群优化(PSO)算法的仿真对抗测试结果表明,所提出的机动决策方法能够在近距空战中有效击败对手,产生更为灵活的欺骗性机动行为。

Group Coevolution and Immigration Pigeon-Inspired Optimized Dual-layer Controller for Aerial Manipulator Trajectory Tracking

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.

Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization

This paper develops a novel optimization method oriented to the resilience of multiple Unmanned Aerial Vehicle(multi-UAV)formations to achieve rapid and accurate reconfiguration under random attacks.First,a resilience metric is applied to reflect the effect and rapidity of multi-UAV formation resisting random attacks.Second,an optimization model based on a parameter optimization problem to maximize the system resilience is established.Third,an Adaptive Learning-based Pigeon-Inspired Optimization(ALPIO)algorithm is designed to optimize the resilience value.Finally,typical formation topologies with six UAVs are investigated as a case study to verify the proposed approach.The experimental results indicate that the proposed scheme can achieve resilience optimization for a multi-UAV formation reconfiguration by increasing the system resilience values to 97.53%and 81.4%after random attacks.

Multilevel Image Thresholding Using Tsallis Entropy and Cooperative Pigeon-inspired Optimization Bionic Algorithm

Multilevel thresholding is a simple and effective method in numerous image segmentation applications.In this paper,we propose a new multilevel thresholding method that uses cooperative pigeon-inspired optimization algorithm with dynamic distance threshold(CPIOD)for boosting applicability and the practicality of the optimum thresholding techniques.Firstly,we employ the cooperative be havior in the map and compass operator of the pigeon-inspired optimization algorithm to overcome the"curse of dimensionality"and help the algorithm converge fast.Then,a distance threshold is added to maintain the diversity of the pigeon population and increase the vitality to avoid local optimization.Tsallis entropy is used as the objective function to evaluate the optimum thresholds for the considered gray scale images.Four benchmark images are applied to test the property and the stability of the proposed CPIOD algorithm and three other optimization algorithms in multilevel thresholding problems.Segmentation results of four optimization algorithms show that CPIOD algorithm can not only get higher quality segmentation results,but also has better stability.

Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization

We propose multi-objective social learning pigeon-inspired optimization(MSLPIO)and apply it to obstacle avoidance for unmanned aerial vehicle(UAV)formation.In the algorithm,each pigeon learns from the better pigeon but not necessarily the global best one in the update process.A social learning factor is added to the map and compass operator and the landmark operator.In addition,a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting.We simulate the flight process of five UAVs in a complex obstacle environment.Results verify the effectiveness of the proposed method.MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.

Fractional Order Darwinian Pigeon-Inspired Optimization for Multi-UAV Swarm Controller

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.

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

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.

Hawk and pigeon's intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

For unmanned aerial vehicle(UAV)swarm dynamic combat,swarm antagonistic motion control and attack target allocation are extremely challenging sub-tasks.In this paper,the competitive learning pigeon-inspired optimization(CLPIO)algorithm is proposed to handle the cooperative dynamic combat problem,which integrates the distributed swarm antagonistic motion and centralized attack target allocation.Moreover,the threshold trigger strategy is presented to switch two sub-tasks.To seek a feasible and optimal combat scheme,a dynamic game approach combined with hawk grouping mechanism and situation assessment between sub-groups is designed to guide the solution of the optimal attack scheme,and the model of swarm antagonistic motion imitating pigeon’s intelligence is proposed to form a confrontation situation.The analysis of the CLPIO algorithm shows its convergence in theory and the comparison with the other four metaheuristic algorithms shows its superiority in solving the mixed Nash equilibrium problem.Finally,numerical simulation verifis that the proposed methods can provide an effective combat scheme in the set scenario.

Human resource allocation for multiple scientific research projects via improved pigeon-inspired optimization algorithm

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.

Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm

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-

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

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.