Abstract：An optimization algorithm is proposed based on the simulation of flight modes of the real birds, namely particle swarm optimization algorithm with double-flight modes(DMPSO). Particles can use maneuver flight-mode or non-maneuver flight-mode to fly during searching. Each particle chooses its flight-mode according to the feedback of the swarm information and its own state in the search. To test the performance of DMPSO, experiments are carried out on some typical complex high-dimensional optimization problems. The experimental results show that the DMPSO avoids the premature convergence problems and it is effective when solving complex high dimensional optimization problems.
[1] Kennedy J, Eberhart R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, USA, 1995, IV: 1942-1948 [2] Parsopoulos K E, Vrahatis M N. On the Computation of All Global Minimizers through Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 211-224 [3] van den Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 225-239 [4] Lpvbjerg M, Rasmussen T K, Krink T. Hybrid Particle Swarm Optimizer with Breeding and Subpopulation // Proc of the Genetic and Evolutionary Computation Conference. San Francisco, USA, 2001: 469-476 [5] Suganthan P N. Particle Swarm Optimiser with Neighborhood Operator // Proc of the Congress on Evolutionary Computation. Washington, USA, 1999, Ⅲ: 1958-1962 [6] Kennedy J, Mendaes R. Neighborhood Topologies in Fully Informed and Best-of-Neighborhood Particle Swarms. IEEE Trans on Systems, Man and Cybernetics: Part C, 2006, 36(4): 515-519 [7] Liang J J, Suganthan P N. Dynamic Multi-swarm Particle Swarm Optimizer // Proc of the IEEE Swarm Intelligence Symposium. Pa-sadena, USA, 2005: 124-129 [8] Shi Y H, Eberhart R C. Empirical Study of Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. Wa-shington, USA, 1999, III: 1945-1950 [9] Ratnaweera A, Halgamuge S, Watson H C. Self-organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Trans on Evolutionary Computation, 2004, 8 (3): 240-255 [10] Lü Q, Liu S R, Qiu X N. Design and Realization of Particle Swarm Optimization Based on Pheromone Mechanism. Acta Automatica Sinica, 2009, 35(11): 1410-1419 (in Chinese) (吕 强,刘士荣,邱雪娜.基于信息素机制的粒子群优化算法的设计与实现.自动化学报, 2009, 35(11): 1410 -1419) [11] Lü Q, Liu S R. A Particle Swarm Optimization Algorithm with Fully Communicated Information. Acta Electronica Sinica, 2010, 38(3): 664-667 (in Chinese) (吕 强,刘士荣.一种信息充分交流的粒子群优化算法.电子学报, 2010, 38(3): 664-667) [12] Liang J J, Qin A K, Suganthan P N, et al. Comprehensive Lear-ning Particle Swarm Optimizers for Global Optimization of Multimodal Functions. IEEE Trans on Evolutionary Computation, 2006, 10(3): 281-295
[7] Kennedy J, Mendes R. Population Structure and Particle Swarm Performance // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002: 1671-1676 [9] Wang Xuefei, Wang Fang, Qui Yuhui. Research on a Novel Particle Swarm Algorithm with Dynamic Topology. Computer Science, 2007, 34(3): 205-207 (in Chinese) (王雪飞,王 芳,邱玉辉.一种具有动态拓扑结构的粒子群算法研究.计算机科学, 2007, 34(3): 205-207) [10] Shi Yuhui, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization // Proc of the Congress on Evolutionary Computaton. Piscataway, USA, 2001: 101-106 [12] Lü Suzhen, Hou Zhirong. Particle Swarm Optimization with Adaptive Mutation. Acta Electronica Sinica ,2004, 32(3): 416-420 (in Chinese) (吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报, 2004, 32(3): 416-420) [13] He Ran, Wang Yongji, Wang Qing, et al. An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity. Journal of Software, 2005, 16(12): 2036-2044 (in Chinese) (赫 然,王永吉,王 青,等.一种改进的自适应逃逸微粒群算法及实验分析.软件学报, 2005, 16(12): 2036-2044) [14] Jiao Bin, Lian Zhigang, Gu Xingsheng. A Dynamic Inertia Weight Particle Swarm Optimization Algorithm. Chaos, Solitons & Fractals, 2008, 37 (3): 698-705 [16] Sun Jun, Xu Wenbo, Feng Bin. A Global Search of Quantum-Behaved Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. Washington, USA, 2004, I: 325-331 [17] Cong Lin,Sha Yuheng,Jiao Licheng. Organizational Evolutionary Particle Swarm Optimization for Numerical Optimization. Pattern Recognition and Artifical Intelligence, 2007, 20(2): 145-153 (in Chinese) (丛 琳,沙宇恒,焦李成.组织进化粒子群数值优化算法.模式识别与人工智能, 2007, 20(2): 145-153) [19] Shelokar P S ,Siarry P, Jayaraman V K, et al. Particle Swarm and Ant Colony Algorithms Hybridized for Improved Continuous Optimization. Applied Mathematics and Computation, 2007, 188 (1) : 129-142 [21] Lü Qiang, Qiu Xuena, Liu Shirong. A Discrete Particle Swarm Optimization Algorithm with Fully Communicated Information // Proc of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation. Shanghai, China, 2009: 393-400 [23] Shao Zengzhen, Wang Hongguo, Liu Hong. Dimensionality Reduction Symmetrical PSO Algorithm Characterized by Heuristic Detection and Self-Learning. Computer Science, 2010, 37(5): 219-222 (in Chinese) (邵增珍,王洪国,刘 弘.具有启发式探测及自学习特征的降维对称粒子群算法.计算机科学, 2010, 37(5): 219-222)
2014, Vol. 27 
