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Quantum-Behaved Loser Reverse-Learning Differential Evolution Algorithm-Based Path Planning for Unmanned Aerial Vehicle.

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Bibliographic Details
Title: Quantum-Behaved Loser Reverse-Learning Differential Evolution Algorithm-Based Path Planning for Unmanned Aerial Vehicle.
Authors: Chen, Zhuoyun, Zhang, Xiangyin, Lu, Yao
Source: Actuators; Feb2026, Vol. 15 Issue 2, p74, 28p
Subject Terms: DIFFERENTIAL evolution, ROBOTIC path planning, FLIGHT planning (Aeronautics), OPTIMIZATION algorithms, DRONE aircraft
Abstract: This paper proposes the Quantum-behaved Loser Reverse-learning Differential Evolution (QLRDE) algorithm to address the inherent limitations of the standard Differential Evolution (DE) algorithm, including slow convergence speed and the premature stagnation in local optima. QLRDE incorporates three innovations: quantum-behaved mutation strategies suppress premature convergence by leveraging quantum mechanics, the Loser Reverse-Learning Mechanism enhances diversity by reconstructing inferior individuals through opposition-based learning, and an adaptive parameter adjustment mechanism balances exploration and exploitation to improve robustness and convergence efficiency. Experimental evaluations on twelve benchmark functions confirm that QLRDE demonstrates better performance than existing algorithms in terms of search capability and convergence speed. Furthermore, QLRDE is employed for the 3D UAV path planning problem. QLRDE can generate B-Spline-based smooth flight paths and incorporate real-world constraints into the cost function. Simulation results confirm that QLRDE outperforms several competing algorithms with respect to path quality, computational efficiency, and robustness. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:This paper proposes the Quantum-behaved Loser Reverse-learning Differential Evolution (QLRDE) algorithm to address the inherent limitations of the standard Differential Evolution (DE) algorithm, including slow convergence speed and the premature stagnation in local optima. QLRDE incorporates three innovations: quantum-behaved mutation strategies suppress premature convergence by leveraging quantum mechanics, the Loser Reverse-Learning Mechanism enhances diversity by reconstructing inferior individuals through opposition-based learning, and an adaptive parameter adjustment mechanism balances exploration and exploitation to improve robustness and convergence efficiency. Experimental evaluations on twelve benchmark functions confirm that QLRDE demonstrates better performance than existing algorithms in terms of search capability and convergence speed. Furthermore, QLRDE is employed for the 3D UAV path planning problem. QLRDE can generate B-Spline-based smooth flight paths and incorporate real-world constraints into the cost function. Simulation results confirm that QLRDE outperforms several competing algorithms with respect to path quality, computational efficiency, and robustness. [ABSTRACT FROM AUTHOR]
ISSN:20760825
DOI:10.3390/act15020074