路径规划问题的自适应改进樽海鞘群算法求解研究

Research on adaptive improved salp swarm algorithm for path planning problem

路径规划问题的求解具有理论与实际应用价值.为寻到最短路径,解决传统算法存在收敛速度不快,寻优精度不高和全局最优值易陷入局部最优解区域的问题,提出一种基于扰动因子和自适应惯性权重的改进樽海鞘群算法(DISSA).首先,在领导者位置更新阶段添加扰动因子,扩大搜索范围来提高局部搜索能力,引导个体探索其他位置,以增加种群的多样性.其次,利用上一代的最优位置来代替上一代前一个体的位置对跟随者的位置更新进行改进,以解决跟随者盲目跟从的问题,并进一步加强算法的局部搜索能力.再次,在改进的跟随者位置更新阶段,引入负双曲正切函数控制的惯性权重来平衡算法的全局搜索和局部搜索能力.选取12个基准测试函数进行仿真实验,对比樽海鞘群算法(SSA)、粒子群算法(PSO)、蚁狮优化算法(ALO)和乌燕欧优化算法(STOA),实验结果表明,所提算法能够有效加快收敛速度,提高寻优精度.最后,将改进算法应用于路径规划问题中,结果证明了该算法较其他算法所寻路径更优.

The solution of path planning problem has theoretical and practical application value.In order to find the shortest path and solve the problems of the traditional algorithm,such as the slow convergence rate,the low precision of optimization and the fact that the global optimal value is easy to fall into the local optimal solution region,this paper proposes an improved salp swarm algorithm based on the disturbance factor and the adaptive inertia weight(DISSA).Firstly,a disturbance factor was added in the updating stage of the leader position to expand the search range to improve the local search ability.By guiding individuals to explore other positions,the diversity of the population was increased.Secondly,the follower's position update is improved by replacing the previous generation's optimal position with the previous generation's position,so as to solve the problem of follower's blind following,and further strengthen the local search ability of the algorithm.Then the inertia weight controlled by hyperbolic tangent function is introduced in the improved follower position update stage to balance the global search and local search capabilities of the algorithm.By selecting 12 benchmark test functions for simulation experiments,and comparing with Salp Swarm Optimization(SSA),Particle Swarm Optimization(PSO),Ant Lion Optimization(ALO)and Sooty Tern Optimization Algorithm(STOA),the experimental results show that the proposed algorithm can effectively accelerate the convergence speed and improve the optimization accuracy.Finally,the improved algorithm is applied to the path planning problem,and the results show that the proposed algorithm can find a better path than other algorithms.