Global path planning using modified firefly algorithm
Global path planning using modified firefly algorithm
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DOI：
10.1109/mhs.2017.8305195
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Xiaochao Chen;M. Zhou;Jian Huang;Zhiwei Luo
中科院分区：
文献类型：
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作者：
Xiaochao Chen;M. Zhou;Jian Huang;Zhiwei Luo
萤火虫算法广泛应用于优化问题的求解。为了解决多目标下的最优路径问题，提出了一种基于改进萤火虫算法的全局路径规划算法。针对标准萤火虫算法（SFA）收敛速度慢、局部搜索不准确的问题，提出用高斯随机游动代替固定步长的SFA，以提高其随机搜索能力。在迭代过程中采用双重检查的方法，提高了每只萤火虫移动的成功率。为了测量任意两个萤火虫（代表两条路径）之间的距离，提出了路径中心（PC）的概念和计算方法。仿真结果表明，与粒子群优化算法（PSO）和SFA相比，该算法在收敛速度和精度方面均优于这两种算法。
Firefly algorithm is widely used in the tackling of optimization problems. This paper proposed a global path planning algorithm based on the modified firefly algorithm (PPMFA) in order to find an optimal path under multiple objective functions. Owning to the low convergence speed and inaccurate local search ability of the standard firefly algorithm (SFA), the Gaussian random walk is proposed to replace the fixed step size of the SFA so as to improve the random search ability. Incorporating a double check method during the iteration process, the success rate of the movement of each firefly is increased. In order to measure the distance between any two fireflies (which represent two paths), the conception and calculation method of Path Center (PC) is proposed. Simulation results show that compared with the particle swarm optimization (PSO) and SFA, the proposed algorithm outperforms both of the algorithms in terms of convergence speed and accuracy.