Search Results (30)

To address the dynamic decision-making and control problem in unmanned aerial vehicle (UAV) swarms, this paper proposes a cumulative prospect theory-driven pigeon-inspired optimization (CPT-PIO) algorithm. Gray relational analysis and information entropy theory are integrated into cumulative prospect theory (CPT), constructing a prospect value model for Pareto solutions by setting reference points, defining value functions, and determining attribute weights. This prospect value is used to evaluate the quality of each Pareto solution and serves as the fitness function in the pigeon-inspired optimization (PIO) algorithm to guide its evolutionary process. Furthermore, incorporating individual and swarm situation assessment methods, the situation assessment model is constructed and the information entropy theory is employed to ascertain the weight of each assessment index. Finally, the reverse search mechanism and competitive learning mechanism are introduced into the standard PIO to prevent premature convergence and enhance the population’s exploration capability. Simulation results demonstrate that the proposed CPT-PIO algorithm significantly outperforms two novel multi-objective optimization algorithms in terms of search performance and solution quality, yielding higher-quality Pareto solutions for dynamic UAV swarm decision-making. Full article

The continuous configuration changes and velocity variations of tilt-rotor UAVs during the transition phase pose significant challenges to flight safety. Hence, the transition phase trajectory must be specially designed. The transition corridor is an effective means of characterizing the controllable flight state and safe flight boundary of the tilt-rotor UAV transition phase. However, the conventional transition corridor is established based on the trim criterion, which cannot fully characterize the dynamic characteristics of the transition phase, resulting in deviations in the delineation of the flight boundary. This paper proposes a method that characterizes the dynamic transition corridor of a tilt-rotor UAV during the transition phase. A three-dimensional transition corridor considering the nacelle angle, velocity, and angle of attack is established by relaxing the force constraints and introducing angle of attack variables, allowing the dynamic characteristics of acceleration and deceleration in the transition phase to be characterized. On this basis, a transition trajectory optimization method based on the three-dimensional dynamic transition corridor is established using pigeon-inspired optimization with an objective that considers the smooth transition of tilt-rotor UAVs. Numerical simulations show that, compared with the transition trajectory obtained using a two-dimensional transition corridor, the proposed method ensures smoother changes in the velocity, nacelle angle, and expected angle of attack during the transition phase, resulting in stronger engineering practicality. Full article

To guide the movement of a UAV swarm in an obstacle-dense environment, a curved regular virtual tube based on pigeon-inspired optimization (PIO) is planned in this paper. There is no obstacle within the virtual tube, which serves as a safe corridor for UAVs. Then, a distributed swarm controller based on a pigeon flocking hierarchical model is proposed, enabling all UAVs to pass through a virtual tube, guaranteeing safety between UAVs and keeping within the virtual tube. Numerical simulations demonstrate the effectiveness of the proposed virtual tube planning and UAV swarm passing-through methods. Full article

Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) algorithm. This research proceeds systematically as follows: First, a nonlinear model of fixed-wing UAVs and a decision-making system for swarm air combat are established. Next, a situation function is applied to characterize the battlefield environment and quantify the strategic advantages of each side during the engagement. The LAEPIO algorithm is then advanced to tackle sub-tasks in swarm air combat by incorporating a learning-aided evolutionary mechanism. Building upon this foundation, a swarm maneuver decision method is designed, enabling UAV swarms to select optimal strategies from a library of maneuvers after thoroughly assessing the battlefield scenario. Finally, the efficacy and superiority of the proposed method are demonstrated through comprehensive simulations across diverse air combat scenarios. The results show that the average win rate of the proposed algorithm is 36.7% higher than that of similar algorithms. Full article

This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired optimization (PIO) is proposed, which incorporates a bionic social learning strategy. In this modification, the global best is replaced by the average of the top-ranked solutions in the map and compass operator, while the global center is replaced by the local center in the landmark operator. The paper also proves the algorithm’s convergence and provides complexity analysis. Comparison experiments demonstrate that the proposed method searches for the optimal solution while guaranteeing fast convergence. Subsequently, a path-planning model, detection units’ network model, and cost estimation are constructed. The developed BSLSPIO is utilized to generate feasible paths for UAVs, adhering to time consistency constraints. The simulation results show that the BSLSPIO generates feasible paths at minimum cost and effectively solves the UAVs’ cooperative path-planning problem. Full article

The Intelligent Optimization Algorithm (IOA) is widely focused due to its ability to search for approximate solutions to the NP-Hard problem. To enhance applicability to practical scenarios and leverage advantages from diverse intelligent optimization algorithms, the Hybrid Intelligent Optimization Algorithm (H-IOA) is employed. However, IOA typically requires numerous iterations and substantial computing resources, resulting in poor execution efficiency. In complex optimization scenarios, IOA traditionally relies on population partitioning and periodic communication, highlighting the feasibility and necessity of parallelization. To address the challenges above, this paper proposes a general hardware design approach for H-IOA based on multi-FPGA. The approach includes the hardware architecture of multi-FPGA, inter-board communication protocols, population storage strategies, complex hardware functions, and parallelization methodologies, which enhance the computing capabilities of H-IOA. To validate the proposed approach, a case study is conducted, in which an H-IOA integrating genetic algorithm (GA), a simulated annealing algorithm (SA), and a pigeon-inspired optimization algorithm (PIO) are implemented on a multi-FPGA platform. Specifically, the flexible job-shop scheduling problem (FJSP) is employed to verify the potential in industrial applications. Two Xilinx XC6SLX16 FPGA chips are used for hardware implementation, encoded in VHDL, and an AMD Ryzen 7 5800U was used for the software implementation of Python programs (version 3.12.4). The results indicate that hardware implementation is 13.4 times faster than software, which illustrates that the proposed approach effectively improves the execution performance of H-IOA. Full article

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method. Full article

The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R² = 0.8055, MAE = 8.8475 Mg·ha⁻¹, RMSE = 12.2876 Mg·ha⁻¹, relative RMSE = 18.1715%; Category-2: R² = 0.7956, MAE = 1.7103 Mg·ha⁻¹, RMSE = 2.2887 Mg·ha⁻¹, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types. Full article

In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss. Full article

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network’s learning rate to derive the optimal group’s weight information and ultimately improve the model’s detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. Full article

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm. Full article

In today’s world, a situational awareness of sustainability is becoming increasingly important. Leaving a better world for future generations is becoming the main interest of many studies. It also puts pressure on managers to change production methods in most industries. Reducing carbon emissions in industry today is crucial to saving our planet. Theoretical research and practical industry requirements diverge, even though numerous researchers have tackled various strategies to handle carbon emission problems. Therefore, this work considers the carbon emission problem of the furniture manufacturing industry in Hosur, Tamilnadu, India. The case study company has a manufacturing system that resembles a hybrid flow shop (HFS) environment. As the HFS scheduling problems are NP-hard in nature, exact solution techniques could not be used to solve the problems. Hence, a sequential hybrid optimization algorithm (SHOA) has been developed in this paper to minimize the carbon footprint. In the SHOA, the pigeon-inspired optimization algorithm (PIOA) is hybridized sequentially with the firefly algorithm (FA). A computational experimental design is proposed to analyze the efficiency of the introduced strategy, and the solutions indicate that the developed approach could reduce the carbon footprint by up to 9.82%. The results motivate us to implement the proposed algorithm in the manufacturing industry to reduce the carbon footprint. Full article

In this paper, the problem of broadband vibration suppression of power-driven-damper vehicle “inerter-spring-damper” (ISD) suspension is studied. The suspension can effectively inherit the low-frequency vibration suppression effect of ISD suspension and the high-frequency vibration suppression effect of the power-driven-damper control strategy. Based on the structural method, this paper proposes four suspensions with different structures. The optimal structure and parameters are obtained by using pigeon-inspired optimization. The results show that, based on the optimal structure, the Root-Mean-Square (RMS) of body acceleration and the RMS of suspension working space are reduced by 23.1% and 6.6%, respectively, compared to the traditional passive suspension. The influence of the damping coefficient on the dynamic performance of the power-driven-damper vehicle ISD suspension is further studied. The vibration suppression characteristics of the proposed suspension are simulated and analyzed in both the time domain and frequency domain. It is shown that the power-driven-damper vehicle ISD suspension can effectively reduce vibrations across a wide frequency range and significantly improve body acceleration and suspension working space, thereby enhancing the ride comfort. Full article

This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV. Full article

The laser gyroscope of Laser Inertial Navigation System (LINS) eliminates the influence of the locked zone with mechanical dither. The output information of laser gyroscopes must be filtered before use to eliminate vibration noise. Laser gyroscope filters are designed according to the instrument accuracy, calculation capacity, vibration frequency, system dynamic characteristics, and other indicators. In this paper, a pigeon-inspired optimization (PIO) method is proposed for use in filter design. The PIO method can flexibly design filters with excellent performance according to the indicator requirements. In the method, the constraints and indicators of the amplitude, phase and order of the LINS filter are firstly confirmed according to the application requirements; then, the objective function is established, and the parameters to be optimized of the PIO are set according to the order of the filter; finally, the PIO method is used to obtain filter parameters that can satisfy the constraints and achieve better performance. Referring to the idea of biological evolution mechanisms, we propose a new improved pigeon-inspired optimization method based on natural selection and Gaussian mutation (SMPIO), which can obtain more stable results and higher accuracy. In the SMPIO method, the particle swarm is firstly selected by natural selection, that is, the particles are sorted according to the fitness function, and some particles with poor fitness are replaced by those with better fitness; then, all particles are subjected to Gaussian mutation to obtain a better global optimum. SMPIO method can flexibly design filters according to the comprehensive requirements of laser gyro performance and navigation control indicators, which cannot be achieved by traditional filter design methods; the improvement based on natural selection and Gaussian mutation enables SMPIO to have faster convergence speed, and higher accuracy. Full article