Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Ajith, V. S.; G, Jolly K

doi:10.1080/01969722.2022.2157607

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…ACO using a similar hypothesis is used in hilly regions when choosing paths Chaurasia et al (2023); Nguyen (2022). For this reason, ACO is more useful in hilly areas than other algorithms Yang et al (2022); Ajith and Jolly (2022). ACO may be thought of as a distributed multi-objective searching for independent solutions simultaneously at multiple points in the problem space, which makes the algorithm have a high research ability Liang et al (2021a); Kwa et al (2022); Leon-Blanco et al (2022).…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Optimization to Developing Hilly Areas Tourism Path under Dynamic Objectives

Damos

Zhu

et al. 2023

Preprint

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Tourism path planning is complex in hilly areas due to a variety of challenges including steep hills, narrow roads, and limited accessibility. Furthermore, when planning tourism paths, hilly areas frequently have distinctive natural potential that must be carefully taken into account such as dynamic objectives. Temperature, pressure, and health state are three of the most important dynamic objectives. In this paper, we proposed a dynamic objectives approach to developing tourism path planning in hilly areas. The main contribution of this approach was the improved Ant Colony Optimization algorithm ACO to establish a new multi-dynamic objectives approach and apply this approach in hilly areas. We have improved ACO by comparing construction tour and updating pheromone methods. This approach has been applied to develop tourist routes in Jebel Marra hilly in western Sudan. Our approach was compared with the genetic algorithm GA and the traditional ACO. Results show the advantage of our approach due to the optimizing time being 0.27 seconds, 0.45 seconds for GA, and 0.40 seconds for traditional ACO.

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Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Optimization to Developing Hilly Areas Tourism Path under Dynamic Objectives

Damos

Zhu

et al. 2023

Preprint

View full text Add to dashboard Cite

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Hybrid deep learning for object detection in drone imagery: a new metaheuristic based model

Ajith

2023

Multimed Tools Appl

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Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

Mohamed Sameer,

Susitra

2024

Wireless Pers Commun

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Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Cited by 7 publications

References 38 publications

An Improved Ant Colony Optimization to Developing Hilly Areas Tourism Path under Dynamic Objectives

An Improved Ant Colony Optimization to Developing Hilly Areas Tourism Path under Dynamic Objectives

Hybrid deep learning for object detection in drone imagery: a new metaheuristic based model

Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

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