Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning

Multiple unmanned aerial vehicle (UAV) path planning is the benchmark problem of multiple UAV application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAV to fly safely to the goal position according to their specific start position in three-dimens...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 28; H. 7-8; S. 6185 - 6198
Hauptverfasser: Chen, Yang, Pi, Dechang, Wang, Bi, Mohamed, Ali Wagdy, Chen, Junfu, Wang, Yintong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
Springer Nature B.V
Schlagworte:
Algorithms
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Control
Energy consumption
Engineering
Equilibrium
Global optimization
Heuristic methods
Learning
Mathematical functions
Mathematical Logic and Foundations
Mechatronics
Optimization algorithms
Parameter identification
Path planning
Planning
Polynomials
Robotics
Unmanned aerial vehicles
Multi-UAV path planning
Equilibrium optimizer
Global optimization
Numerical optimization
ISSN:1432-7643, 1433-7479
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Zusammenfassung:Multiple unmanned aerial vehicle (UAV) path planning is the benchmark problem of multiple UAV application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAV to fly safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. To improve the optimization ability of EO to solve high-dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. First, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09471-4