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1 December 2023 Research on application of coevolutionary ant colony optimization algorithm based on hybrid breeding strategy
Chun Liu, Xiaomin Li, Zhiwei Ye, Xiaobing Chen, Zhengzhong Pan, Wei Jiang, Jin Huang
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Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 129402V (2023) https://doi.org/10.1117/12.3011157
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China
Abstract
Ant Colony Optimization (ACO) is a classic swarm intelligence optimization algorithm that has been widely applied in various task scheduling scenarios. However, traditional ACO may easily get trapped in local optimal solutions. Inspired by Hybrid Breeding Optimization (HBO) algorithm and coevolution, this paper proposes a Heterogeneous Coevolution Ant Colony Optimization (HCEACO) algorithm based on hybrid breeding mechanisms to overcome the shortcomings of a single population in terms of solution diversity. Moreover, a strategy based on population similarity is proposed to determine whether communication is necessary after a fixed number of iterations, and to maintain a dynamic balance between population diversity and convergence speed in selecting communication partners. To fully validate the effectiveness of the proposed algorithm, multiple path planning algorithms are simulated and applied to multi-load Automatic Guided Vehicle (AGV) path planning. The experimental results show that the improved algorithm performs well in the multi-load AGV path planning problem, and has broad application prospects in this field.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chun Liu, Xiaomin Li, Zhiwei Ye, Xiaobing Chen, Zhengzhong Pan, Wei Jiang, and Jin Huang "Research on application of coevolutionary ant colony optimization algorithm based on hybrid breeding strategy", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 129402V (1 December 2023); https://doi.org/10.1117/12.3011157
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KEYWORDS
Mathematical optimization
Particle swarm optimization
Evolutionary optimization
Computer simulations
Detection and tracking algorithms
Design and modelling
Reliability
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