多种群合作学习的多模态多目标路径规划算法

An algorithm based on multi-swarm cooperative learning was proposed to plan multiple optimal paths to meet multiple objectives, which can improve the robustness and practicability of the planned paths. The concept of the particle swarm optimization algorithm served as the algorithm's guidance. First, to address the issue that a single population is easy to trap in local optimum in the multi-dimensional target space, a strategy of sub-swarm division was proposed. The population was divided into many sub-swarms according to the number of objectives, balancing the searching ability of the algorithm in each dimension of the target space. Second, key path points were extracted according to the in-degree and out-degree of the path points in the map. In the coding process, real coding was used to initialize the population. The dimension of the path code was equal to the number of key path points, reducing the size of the solution space. In the decoding process, the decoding experience of the elite solutions guided the fast search for feasible solutions. This method can transfer the decoding experience efficiently and reduce the uncertainty of decoding, which improved the optimization ability of the algorithm. Finally, the search results of all sub-swarms were sorted by the non-dominated sorting method to obtain the paths satisfying the planning objectives. The path planning algorithm based on the multi-swarm cooperative learning outperforms the standard particle swarm optimization algorithm in terms of search and optimization ability and is capable of solving the multimodal multi-objective path planning problem.