Improved chimp optimization algorithm for three-dimensional path planning problem

The three-dimensional (3D) path planning of unmanned aerial vehicle (UAV) focuses on avoiding collision with obstacles and finding the optimal path to reach the target position in the complex environment. An improved Chimp optimization algorithm (IChOA) based on somersault foraging strategy with ada...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 81; no. 19; pp. 27397 - 27422
Main Authors: Du, Nating, Zhou, Yongquan, Deng, Wu, Luo, Qifang
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2022
Springer Nature B.V
Subjects:
Algorithms
Collision avoidance
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Foraging behavior
Hunting
Intelligence
Linear programming
Multimedia
Multimedia Information Systems
Optimization
Optimization algorithms
Path planning
Planning
Population
Special Purpose and Application-Based Systems
Strategy
Unmanned aerial vehicles
Metaheuristic
Chimp optimization algorithm
Dynamic weighting factor
Somersault foraging strategy
Three-dimensional path planning
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:The three-dimensional (3D) path planning of unmanned aerial vehicle (UAV) focuses on avoiding collision with obstacles and finding the optimal path to reach the target position in the complex environment. An improved Chimp optimization algorithm (IChOA) based on somersault foraging strategy with adaptive weight was proposed to solve the three-dimensional path planning problem. Firstly, the position vector updating equation was dynamically adjusted by introducing the weighting factor derived from coefficient vector of the ChOA. Secondly, the somersault foraging strategy was introduced to prevent the group from falling into a local optimum in the later stage, and at the same time, the population diversity in the early stage was slightly improved. The algorithm was tested on CEC2019 functions and three-dimensional path planning. Compared with other methods, the results show that this algorithm can provide more competitive results.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-12882-4