Abstract: Aiming at the shortcomings of Golden Jackal Optimization(GJO) algorithm,such as low optimization accuracy and slow convergence speed,an improved GJO algorithm based on Particle swarm (PGJO) was proposed.The population was initialized using a combination of Chebyshev chaotic mapping and elite selection strategy to improve the quality of initial solutions.Then,a new search method was proposed based on the idea of Particle Swarm Optimization(PSO).By adopting a dynamic transformation strategy,whether PGJO used the original Levy search method or a new search method to update individual positions was determined for improving the convergence accuracy of the algorithm.A population convergence stagnation monitoring strategy was proposed to enhance the global search ability of the algorithm.Comparing PGJO with other intelligent optimization algorithms through 11 benchmark test functions,the results showed that the algorithm performance better than other algorithms.The engineering application ability of this algorithm was verified by the reduction of path length by 3.4% and the decrease of the number of inflection points by 21% in unmanned aerial vehicle path planning compared to other algorithms.

Key words: intelligent optimization algorithm, golden jackal optimization algorithm, convergence monitoring strategy for populations, Chebyshev chaotic mapping, three-dimensional path planning

摘要： 为了解决传统金豺算法收敛精度低,搜索速度慢等问题,提出一种融合粒子群算法的改进金豺优化算法(PGJO)。首先,采用Chebyshev混沌映射和精英选择策略结合的方式对种群进行初始化,提高初始解质量;然后,结合粒子群优化算法(PSO)思想,提出一个新的搜索方式。采用动态转换策略,判断PGJO采用原Levy方式搜索还是采用新的搜索方式更新个体位置,以提高算法收敛精度;最后,提出了种群收敛监测策略,帮助算法跳出局部最优。将PGJO与其他智能优化算法经过11个基准测试函数对比实验表明,算法性能均优于其他算法。将PGJO应用于无人机路径规划当中,对比其他算法路径长度下降了3.4%,拐点个数减少了21%,验证了该算法的工程应用能力。

关键词: 智能优化算法, 金豺优化算法, 种群收敛监测策略, Chebyshev混沌映射, 三维路径规划