Predator-Prey Pigeon-Inspired Optimization for UAV ALS Longitudinal Parameters Tuning

Duan, Haibin; Huo, Mengzhen; Yang, Zhiyuan; Shi, Yuhui; Luo, Qiwu

doi:10.1109/taes.2018.2886612

Cited by 26 publications

(8 citation statements)

References 34 publications

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“…Zhang and Duan [37] proposed Social Class Pigeon Inspired Optimization (SCPIO) with Time Stamp Segmentation (TSS) for multi-UAV cooperative path planning. Duan et al [38] used PPPIO optimization algorithm to improve the tracking control of the fixedwing UAV. Qiu and Duan [39] applied MPIO for stable formation of UAV'sin complex environment.…”

Section: Comparative Analysis Of Pigeon Inspired Optimization and Its Variants For Motion Planningmentioning

confidence: 99%

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Shafiq¹,

Ali²,

Alkhammash³

2022

Motion Planning

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Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies.

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Section: Comparative Analysis Of Pigeon Inspired Optimization and Its Variants For Motion Planningmentioning

confidence: 99%

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Shafiq¹,

Ali²,

Alkhammash³

2022

Motion Planning

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“…其中, 在算法设计上, 鸽群优化算法发展出不同的变体. 例如 Cheng 等提出了一种具有不同拓扑结构的改进鸽群优化算法, 并将环形(Ring)结构应用于地图与指南针算子 [18] ; Yang 等提出了一种基于二进制和实数混合编码的改进鸽群优化算法 [20] [20] ; Duan 等提出了 Predator-Prey 鸽群优化算法 [21] 和变异多目标鸽群优化算法 [22] 等改进方法.…”

Section: 鸽群优化算法unclassified

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

Cheng¹,

Zhang²,

Shi³

et al. 2022

Sci. Sin.-Tech.

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“…In Reference [9], Pareto sort is added into the initialization stage to apply to the multi-objective optimization algorithm to improve the efficiency of the algorithm. The optimization of iterative update mainly has the following three optimization objectives: (1) In order to increase the population diversity, the predator strategy was introduced in the studies of [10][11][12] to increase the diversity of pigeons and overcome the local optimal solution; Deng introduced the concept of dynamics to improve population diversity [13]. (2) In order to solve the problem of the pigeons being easily trapped in the local optimal solution, Duan added the global update operator at the beginning of the iteration, and on this basis, added the population mutation operator further, which can expand the search scope and make the population out of the local optimal solution [14].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Nie³

et al. 2021

Applied Sciences

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Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

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Predator-Prey Pigeon-Inspired Optimization for UAV ALS Longitudinal Parameters Tuning

Cited by 26 publications

References 34 publications

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation

Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

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