Abstract Particle swarm optimization (PSO) is widely studied and applied due to its simple principle and easy implementation. Aiming at improving the convergence speed and the search precision, an algorithm based on PSO, team evolutionary algorithm (TeamEA), is presented.The optimization process of this algorithm is divided into two stages. At the first stage, to keep the diversity the players are divided into junior teams to optimize and the senior team is formed. At the second stage, to improve the convergence speed, only the senior team is optimized. In the process of the whole optimization, by evaluating the achievements of the players, the adjustment of step-length and the maximum space are controlled, and the coaching staff is formed to guide the progress direction of the players. Results on high-dimensional multimodal test functions validate the superiority and effectiveness of the proposed algorithm.
[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] Eberhart R C, Kennedy J. A New Optimizer Using Particle Swarm Theory // Proc of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995: 39-43 [3] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer // Proc of the IEEE World Congress on Computational Intelligence. Anchorage, USA, 1998: 69-73 [4] Shi Y H, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. Seoul,Korea, 2001, I: 101-106 [5] Clerc M. The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. Washington, USA, 1999, III: 1951-1957 [6] Clerc M, Kennedy J. The Particle Swarm-Explosion, Stability and Convergence in a Multidimensional Complex Space. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73 [7] Angeline P J. Using Selection to Improve Particle Swarm Optimization // Proc of the IEEE World Congress on Computational Intelligence. Anchorage, USA, 1998: 84-89 [8] Wang Y S, Ai J B, Shi Y J, et al. Cultural-Based Particle Swarm Optimization Algorithm. Journal of Dalian University of Technology, 2007, 47(4): 539-544 (in Chinese) (王奕首,艾景波,史彦军,等.文化粒子群优化算法.大连理工大学学报, 2007, 47(4): 539-544) [9] Van den Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 225-239 [10] Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans on Systems, Man and Cybernetics, 1979, 9(1): 62-66 [11] Ni Q J, Deng J M, Xing H C. Dynamic Probabilistic Particle Swarm Optimization Based on Heterogeneous Multiple Population Stratery. Pattern Recognition and Artificial Intelligence, 2014, 27(2): 146-152 (in Chinese) (倪庆剑,邓建明,邢汉承.基于异构多种群策略的动态概率粒子群优化算法.模式识别与人工智能, 2014, 27(2): 146-152) [12] Krohling R A, dos Santos C L. PSO-E: Particle Swarm with Exponential Distribution // Proc of the IEEE Congress on Evolutionary Computation. Vancouver, Canada, 2006: 1428-1433 [13] Peram T, Veeramachaneni K, Mohan C K. Fitness-Distance-Ratio Based Particle Swarm Optimization // Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003: 174-181 [14] Sabat S L, Ali L. The Hyperspherical Acceleration Effect Particle Swarm Optimizer. Applied Soft Computing, 2009, 9(3): 906-917 [15] Wang R Y, Hsiao Y T, Lee W P. A New Cooperative Particle Swarm Optimizer with Dimension Partition and Adaptive Velocity Control // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Seoul, Korea, 2012: 103-109 [16] Wolpert D H, Macredy W G. No Free Lunch Theorems for Optimization. IEEE Trans on Evolutionary Computation, 1997, 1(1): 67-82 Suganthan[12]引入空间领域的概念,将处于同个领域内的粒子组成一个子群分别进行优化,并动态改变阈值保证种群多样性.Kumar等[13]分析各种种群拓扑结构对PSO性能的影响,提出设计种群结构的基本原则.
2015, Vol. 28 
