Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning

Zhang, Bo; Duan, Haibin

doi:10.1007/978-3-319-11897-0_12

Cited by 24 publications

(8 citation statements)

References 8 publications

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“…This novel algorithm comprises of multiple self-governing operators: map and compass operator for magnetic field effect of earth and landmark operator for remembering the route with the help natural behavior of homing pigeons. Zhang and Duan [11] proposed a novel Predator-prey pigeon-inspired optimization (PPPIO) for 3-D path planning problem solution of unmanned aerial vehicles (UAVs). Zhang and Duan [12] again proposed improved PIO: PPPIO for 3-D path planning of Uninhabited Combat Aerial Vehicle.…”

Section: Pigeon Inspired Optimization and Its Variantsmentioning

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: Pigeon Inspired Optimization and Its Variantsmentioning

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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“…Omkar et al (2011) introduced a generic method/model based on the ABC for multi-objective design optimisation. Zhang et al (2014a) improved a modified ABC algorithm by changing the employed bee stage and onlooker bee stage to promote the convergence rate. Xu et al (2010a) described a chaotic ABC approach and applied this algorithm for path planning of uninhabited combat air vehicle (UCAV) in various combat fields.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

“…PIO can be implemented in various ways by changing some parameters, and some modifications have been presented to improve its performance. Predator-prey pigeon inspired optimisation (PPPIO) is proposed to solve the three-dimensional path planning and PID controller design problem (Zhang and Duan, 2014a;Sun and Duan, 2014). A novel bloch quantum-behaved pigeon inspired optimisation (BQPIO) is presented by combining the basic PIO with quantum theory (Li and Duan, 2014).…”

Section: Pigeon Inspired Optimisationmentioning

confidence: 99%

New progresses in swarm intelligence-based computation

Duan

Luo

2015

IJBIC

Self Cite

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Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

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“…Homing pigeons can easily locate their homes in accordance with three homing operators: magnetic field, sun, and landmarks. In the context of this algorithm, a map and compass model is formulated according to the magnetic field and sun, while the presentation .of the landmark operator model is done based on landmarks [40]. Note that pigeons perceived the magnetic field through the nose from the magnetic particles taken to the brain by trigeminal nerve [25].…”

Section: Introductionmentioning

confidence: 99%

A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

Bolaji

Okwonu

Shola

et al. 2020

Journal of Intelligent Systems

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AbstractThe pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.

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Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning

Cited by 24 publications

References 8 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

New progresses in swarm intelligence-based computation

A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

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