基于协同聚集和分支偏差的路径优化算法

A Path Optimization Method Based on Co-Aggregation and Branching Deviations

为改善路径规划中优化算法寻优能力不足、易陷入局部最优等问题,基于静态环境提出一种基于协同聚集和分支偏差的路径优化算法(PBAR)。首先,提出分支偏差概念,并综合考虑长度、角度、能耗等因素设计了一种新的适应度函数;其次,提出一种新的协同聚集策略优化种群,增强算法寻优能力;为避免算法陷入局部最优,提出置换策略,同时采用特定方法增强种群的多样性。实验结果表明,PBAR算法能大幅度提高寻优能力和收敛速度,最终规划的路径更加平滑、长度更短。

A route optimization approach(PBAR)based on co-aggregation and branching deviation is developed based on static environment to address the issues of insufficient optimization algorithm searching ability and easy to slip into local optimality in path planning.The approach first puts out the idea of branching deviation and then creates a new fitness function by taking length,angle,and energy usage into account.The population is then optimized,and a new co-aggregation technique is suggested to improve the algorithm′s capacity for optimization finding.A replacement method is suggested to prevent the algorithm from entering a local optimum,and a particular operator is utilized to increase population variety.According to experimental findings,The PBAR algorithm can greatly improve the searching ability and convergence speed,and the final planned path is smoother and shorter.