基于多目标PSO-ACO融合算法的无人艇路径规划

【目的】针对河蟹养殖过程中，水位变化以及无人艇路径规划算法收敛慢、精度低的问题，为提高算法适应性与寻优能力，提出一种多目标粒子群-蚁群融合的无人艇路径规划算法。【方法】首先，分析蟹塘环境及养殖规律等因素，建立静态水深栅格环境模型；其次，针对覆盖遍历式投饵存在局部点投喂不足及路径次优的问题，通过对惯性参数与学习因子的非线性调整，提出基于多目标的改进粒子群算法(Particle swarm optimization, PSO)；然后，调整蚁群算法的初始信息素，并对蚁群算法的信息素挥发因子和启发期望函数自适应改进，提出自适应优化蚁群算法(Ant colony optimization, ACO)；最后，为解决单一算法寻优不足，利用融合PSO-ACO算法，实现无人艇多目标全局路径规划。【结果】仿真结果表明：不同环境投饵策略下，PSO-ACO算法在对多目标路径寻优时，不仅环境适应性好，而且提高了寻优效率和精度，运行时间节省了32%，路径距离缩短了9.78%，迭代次数降低了62.88%，拐点数目减少了44.45%。【结论】所提出多目标点的路径规划算法适用于环境可变的蟹塘养殖，具有较好的应用价值。

Path planning of unmanned vehicle based on multi-objective PSO-ACO fusion algorithm

Objective There are problems in the course of river crab farming due to water level changes as well as slow convergence and low accuracy of the path planning algorithm of unmanned craft. Therefore, a multi-objective particle swarm-ant colony fusion algorithm for unmanned vehicle path planning was presented to improve the adaptability and optimization ability of the algorithm.Method Firstly, the factors such as crab pond environment and breeding law were analyzed, and the environmental model of static water depth in grid was established. Secondly, to cope with the issues of inadequate local point feeding and sub-optimal paths in coverage traversal baiting, a modified particle swarm optimization (PSO) algorithm based on multi-objective was presented by non-linear adjustment of inertia parameters and learning factors. The initial pheromone of the ant colony algorithm was adjusted, and the pheromone volatility factor and heuristic expectation function of the ant colony algorithm were improved to present an adaptive ant colony optimization (ACO) algorithm. Finally, to address the shortcomings of a single algorithm for finding the best, a fusion of PSO-ACO was utilized to realize multi-objective global path planning for baiting vessels.Result The simulation results showed that the PSO-ACO algorithm not only had good environmental adaptability but also improved the efficiency and accuracy of multi-target path finding under different environmental baiting strategies. The PSO-ACO algorithm saved the running time by 32%, shortened the path distance by 9.78%, reduced the number of iterations by 62.88% and reduced the number of inflection points by 44.45%.Conclusion The proposed multi-objective path planning algorithm is suitable for crab pond culture with variable environment, and has good application value.