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.
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