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融合粒子群的改进金豺算法及应用

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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PG-JO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.
Improved golden jackal algorithm based on particle swarm optimization and its application
Aiming at the shortcomings of Golden Jackal Optimization(GJO)algorithm,such as low optimization accu-racy 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 Opti-mization(PSO).By adopting a dynamic transformation strategy,whether PGJO used the original Levy search meth-od 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 applica-tion 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.

intelligent optimization algorithmgolden jackal optimization algorithmconvergence monitoring strategy for populationsChebyshev chaotic mappingthree-dimensional path planning

回立川、曹明远、迟一璇

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辽宁工程技术大学电气与控制工程学院,辽宁 葫芦岛 125105

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

辽宁省教育厅科研项目辽宁省教育厅面上资助项目

LJ2017QL009LJKMZ20220675

2024

10.13196/j.cims.2023.0211

计算机集成制造系统
中国兵器工业集团第210研究所

计算机集成制造系统

CSTPCD北大核心
影响因子：1.092
ISSN：1006-5911
年,卷(期)：2024.30(5)
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