[1] 韩明, 刘教民, 吴朔媚, 等. 粒子群优化的移动机器人路径规划算法[J]. 计算机应用, 2017, 37(8):2258-2263. (HAN M, LIU J M, WU S M, et al. Path planning algorithm of mobile robot based on particle swarm optimization[J]. Journal of Computer Applications, 2017, 37(8):2258-2263.) [2] LI G, CHOU W. Path planning for mobile robot using self-adaptive learning particle swarm optimization[J]. Science China Information Sciences, 2018, 61(5):052204. [3] 贾会群, 魏仲慧, 何昕, 等.基于改进粒子群算法的路径规划[J]. 农业机械学报, 2018, 49(12):371-377. (JIA H Q, WEI Z H, HE X, et al. Path planning based on improved particle swarm optimization[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(12):371-377.) [4] 杨景明, 侯新培, 崔慧慧, 等.多策略改进的多目标粒子群优化算法[J]. 控制与决策, 2017, 32(3):435-442. (YANG J M, HOU X P, CUI H H, et al. Improved multi-objective particle swarm optimization algorithm based on integrating multiply strategies[J]. Control and Decision, 2017, 32(3):435-442.) [5] YU J, LAVALLE S M. Optimal multirobot path planning on graphs:complete algorithms and effective heuristics[J]. IEEE Transactions on Robotics, 2016, 32(5):1163-1177. [6] SAREMI S, MIRJALILI S, LEWIS A. Grasshopper optimisation algorithm:theory and application[J]. Advances in Engineering Software, 2017, 105:30-47. [7] XU Z, DU L, WANG H, et al. Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control[J]. Applied Mathematics and Mechanics (English Edition), 2019, 40(1):113-126. [8] TUMULURU P, RAVI B. GOA-based DBN:grasshopper optimization algorithm-based deep belief neural networks for cancer classification[J]. International Journal of Applied Engineering Research, 2017, 12(24):14218-14231. [9] BARMAN M, DEV CHOUDHURY N B, SUTRADHAR S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145:710-720. [10] MIRJALILI S Z, MIRJALILI S, SAREMI S, et al. Grasshopper optimization algorithm for multi-objective optimization problems[J]. Applied Intelligence, 2018, 48(4):8005-820. [11] 闫旭, 叶春明.混合蝗虫优化算法求解作业车间调度问题[J]. 计算机工程与应用, 2019, 55(6):257-264. (YAN X, YE C M. Hybrid grasshopper optimization algorithm solves job-shop scheduling problem[J]. Computer Engineering and Applications 2019, 55(6):257-264.) [12] 程泽新, 李东生, 高杨.基于蝗虫算法的无人机三维航迹规划[J]. 飞行力学, 2019, 37(2):46-50, 55. (CHENG Z X, LI D S, GAO Y. UAV three-dimensional path planning based on grasshopper algorithm[J]. Flight Dynamics, 2019, 37(2):46-50, 55.) [13] HUA Y, JIN Y, HAO K. A clustering-based adaptive evolutionary algorithm for multiobjective optimization with irregular Pareto fronts[J]. IEEE Transactions on Cybernetics, 2019, 49(7):2758-2770. [14] THABIT S, MOHADES A. Multi-robot path planning based on multi-objective particle swarm optimization[J]. IEEE Access, 2018, 7:2138-2147. [15] MAC T T, COPOT C, TRAN D T, et al. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]. Applied Soft Computing, 2017, 59:68-76. [16] COELLO C A C, LAMONT G B, van VELDHUIZEN D A. Evolutionary algorithms for solving multi-objective problems[M]. Boston:Springer, 2002. [17] SHIM M, SUH M, FURUKAWA T, et al. Pareto-based continuous evolutionary algorithms for multiobjective optimization[J]. Engineering Computations, 2002, 19(1):22-48. [18] FENG W, ZHANG L, YANG S, et al. A multiobjective evolutionary algorithm based on coordinate transformation[J]. IEEE Transactions on Cybernetics, 2019, 49(7):2732-2743. [19] DI L, ZHENG Z, XIA M. Robot path planning based on an improved multi-objective PSO method[C]//Proceedings of the 2016 International Conferenceon Computer Engineering, Information Science & Application Technology. Paris:Atlantis Press, 2016:496-501. |