Abstract：The standard particle swarm optimization is easy to have problems of low convergence speed and precision, prematurity and poor exploring ability during later period. Aiming at these problems, an optimized particle swarm optimization based on improved pigeon search operator is proposed. Population initialization is determined by Beta opposition-based learning strategy, and the diversity of population distribution is realized. The map compass operator is improved by the linear and nonlinear mutation strategy to improve the development and the exploration ability of the pigeon-inspired algorithm. Then, the location and the speed are updated by the improved combination optimization operator to speed up the convergence, enhance the precision and avoid falling into local optimal solution in the particle swarm optimization. Simulation experimental results show that the convergence speed is improved by IPSO, and the accuracy reaches the ideal value set by the functions.
[1] KENNEDY J, EBERHART R C. Particle Swarms Optimization // Proc of the IEEE International Conference on Neural Networks. Washington, USA: IEEE, 1995: 1942-1948. [2] SHI Y H, EBERHART R C. A Modified Particle Swarm Optimizer // Proc of the IEEE International Conference on Evolutionary Computation Proceedings. Washington, USA, IEEE, 1998: 69-73. [3] NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A Novel Particle Swarm Optimization Algorithm with Adaptive Inertia Weight. Applied Soft Computing, 2011, 11(4): 3658-3670. [4] MARINAKIS Y, MIGDALAS A, SIFALERAS A. A Hybrid Particle Swarm Optimization-Variable Neighborhood Search Algorithm for Constrained Shortest Path Problems. European Journal of Operational Research, 2017, 261(3): 819-834. [5] 于 颖,李永生,於孝春.粒子群算法在工程优化设计中的应用.机械工程学报, 2008, 44(12): 226-231. (YU Y, LI Y S, YU X C. Application of Particle Swarm Optimization in the Engineering Optimization Design. Chinese Journal of Mechanical Engineering, 2008, 44(12): 226-231.) [6] 万春秋,王 峻,杨 耕,等.动态评价粒子群优化及风电场微观选址.控制理论与应用, 2011, 28(4): 449-456. (WAN C Q, WANG J, YANG G, et al. Dynamic Evaluation Based Particle Swarm Optimization and Wind Farm Micrositing. Control Theory and Applications, 2011, 28(4): 449-456.) [7] 张国富,蒋建国,齐美彬,等.基于粒子群算法求解多层非线性规划问题.模式识别与人工智能, 2007, 20(6): 745-750. (ZHANG G F, JIANG J G, QI M B, et al. Solutions of Nonlinear Multilevel Programming Based on Particle Swarm Optimization. Pa-ttern Recognition and Artificial Intelligence, 2007, 20(6): 745-750.) [8] DU W B, YING W, YAN G, et al. Heterogeneous Strategy Particle Swarm Optimization. IEEE Transactions on Circuits and Systems II(Express Briefs), 2017, 64(4): 467-471. [9] SAKTHIVEL V P, BHUVANESWARI R, SUBRAMANIAN S. An Improved Particle Swarm Optimization for Induction Motor Parameter Determination. International Journal of Computer Applications, 2011, 1(2): 62-67. [10] CHEN S W, XU Z M, TANG Y, et al. An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight. Mathematical Problems in Engineering,2014. DOI: 10.1155/2014/976486. [11] ZAVALA A E M, AGUIRRE A H, DIHARCE E R V, et al. Constrained Optimization with an Improved Particle Swarm Optimization Algorithm. International Journal of Intelligent Computing and Cybernetics, 2008, 1(3): 425-453. [12] DUAN H B, QIAO P X. Pigeon-Inspired Optimization: A New Swarm Intelligence Optimizer for Air Robot Path Planning. International Journal of Intelligent Computing and Cybernetics, 2014, 7(1): 24-37. [13] BOLAJI A L, BABATUNDE B S, SHOLA P B. Adaptation of Binary Pigeon-Inspired Algorithm for Solving Multidimensional Knapsack Problem // PANT M, RAY K, SHARMA T, et al., eds. Soft Computing: Theories and Applications. Berlin, Germany: Springer, 2017, 538: 743-751. [14] LI H H, DUAN H B. Bloch Quantum-Behaved Pigeon-Inspired Optimization for Continuous Optimization Problems // Proc of the IEEE Chinese Guidance, Navigation and Control Conference. Washington, USA: IEEE, 2014: 2634-2638. [15] ZHANG B, DUAN H B. Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning // Proc of the 5th International Conference on Swarm Intelligence. Berlin, Germany: Springer, 2014: 96-105. [16] ZHANG T J, DUAN H B. A Modified Consensus Algorithm for Multi-UAV Formations Based on Pigeon-Inspired Optimization with a Slow Diving Strategy. CAAI Transactions on Intelligent System, 2017, 12(4): 570-581. [17] 李建平,宫耀华,卢爱平,等.改进的粒子群算法及在数值函数优化中应用.重庆大学学报, 2017, 40(5): 95-103. (LI J P, GONG Y H, LU A P, et al. Application of Improved Particle Swarm Optimization to Numerical Function Optimization. Journal of Chongqing University, 2017, 40(5): 95-103.) [18] 肖文显,刘 震.一种融合反向学习和量子优化的粒子群算法.微电子学与计算机, 2013, 30(6): 126-130. (XIAO W X, LIU Z. Particle Swarm Optimization Based on Opposition-Based Learning and Quantum Optimization. Microelectronics and Computer, 2013, 30(6): 126-130.) [19] 张聚伟,王 宇,杨 挺.基于模糊粒子群算法的有向传感器网络路径覆盖策略.模式识别与人工智能, 2017, 30(2): 183-192. (ZHANG J W, WANG Y, YANG T. Path Coverage Scheme Based on Fuzzy Particle Swarm Optimization Algorithm for Directional Sensor Networks. Pattern Recognition and Artificial Intelligence,2017, 30(2): 183-192.) [20] ZHOU H G, ZHENG C W, HU X H, et al. Path Planner for Unmanned Aerial Vehicles Based on Modified PSO Algorithm // Proc of the IEEE International Conference on Information and Automation. Washington, USA: IEEE, 2008: 541-544. [21] HAO R, LUO D L, DUAN H B. Multiple UAVs Mission Assignment Based on Modified Pigeon-Inspired Optimization Algorithm // Proc of the IEEE Chinese Guidance,Navigation and Control Conference. Washington, USA: IEEE, 2014: 2692-2697. [22] 段海滨,邱华鑫,范彦铭.基于捕食逃逸鸽群优化的无人机紧密编队协同控制.中国科学(技术科学), 2015, 45(6): 559-572.) (DUAN H B, QIU H X, FAN Y M. Unmanned Aerial Vehicle Close Formation Cooperative Control Based on Predatory Escaping Pigeon-Inspired Optimization. Chinese Science(Technological Sciences), 2015, 45(6): 559-572. [23] QIU H X, DUAN H B. Multi-objective Pigeon-Inspired Optimization for Brushless Direct Current Motor Parameter Design. Science China(Technological Sciences), 2015, 58(1): 1915-1923. [24] KENNEDY J, MENDES R. Population Structure and Particle Swarm Performance // Proc of the Congress on Evolutionary Computation. Washington, USA: IEEE, 2002: 1671-1676. [25] MENDES R, KENNEDY J, NEVES J. The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 204-210. [26] RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 240-255. [27] ZHAO S Z, SUGANTHAN P N, PAN Q K, et al. Dynamic Multi-swarm Particle Swarm Optimizer with Harmony Search. Expert Sys- tems with Applications, 2011, 38(4): 3735-3742. [28] LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295. [29] CIUPRINA G, IOAN D, MUNTEANU I. Use of Intelligent-Particle Swarm Optimization in Electromagnetics. IEEE Transactions on Magnetics, 2002, 38(2): 1037-1040. [30] YAO X, LIU Y, LIN G M. Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102. [31] BOX G E P, HUNTER J S, HUNTER W G. Statistics for Experiment: Design, Innovation, and Discovery. Technometrics. 2nd Edition. New York, USA: Wiley, 2005. [32] WOLPERT D H, MACREADY W G. No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82.
2018, Vol. 31 
