Layer bare-bones particle swarm optimization algorithm with few control parameters
ZHANG Fang-fang1,2, WANG Jian-jun1, ZHANG Yong3
1. School of Management, China University of Mining and Technology, Xuzhou 221116, China; 2. Xuzhou Vocational Technology Academy of Finace and Economics, Xuzhou 221006, China; 3. School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Abstract：Aimed at the disadvantage of premature convergence in traditional particle swarm optimization, this paper proposes an improved bare-bones particle swarm optimization algorithm with few parameters, called IBPSO. In this algorithm, a Gaussian distribution based on the global/local best positions is developed to update the particles' positions. It makes unnecessary to perform fine tuning on such control parameters as inertia weight and acceleration coefficients; An update method of the global best position based on chaos disturbance is introduced to maintain the diversity of swarm; Using convergence speed to dynamically assign the mutation probability of each particle, an adaptive jumping operator is designed; And a layer method for updating the position of particle is given to balance the exploitation and exploration abilities of our algorithm. Finally, by optimizing several benchmark functions and comparing with three algorithms, experimental results confirm the effectiveness of the proposed algorithm.
[1] Kennedy J, Eberhart R C. Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks, Piscataway, 1995: 1942-1948. [2] 王洪峰,王娜,汪定伟.一种求解动态多峰优化问题的Memetic粒子群算法[J]. 系统工程理论与实践, 2013, 33(6): 1577-1586. Wang Hongfeng, Wang Na, Wang Dingwei. A memetic particle swarm algorithm for dynamic multi-modal optimization problems[J]. Systems Engineering——Theory & Practice, 2013, 33(6): 1577-1586. [3] 王兆礼, 陈晓宏, 赖成光,等. 基于粒子群规则挖掘算法的洪灾风险评价模型[J]. 系统工程理论与实践, 2013, 33(6): 1615-1621. Wang Zhaoli, Chen Xiaohong, Lai Chengguang, et al. Flood risk assessment model based on particle swarm optimization rule mining algorithm[J]. Systems Engineering——Theory & Practice, 2013, 33(6): 1615-1621. [4] 吕学志,于永利,张柳,等. 资源约束的拼件维修模型与粒子群求解算法[J]. 系统工程理论与实践, 2013, 33(4): 1013-1018. Lü Xuezhi, Yu Yongli, Zhang Liu, et al. Resource constrained cannibalization maintenance model and solving algorithm based on particle swarm optimization[J]. Systems Engineering——Theory & Practice, 2013, 33(4): 1013-1018. [5] 王廷梁, 王理, 夏国平. 基于混合粒子群算法的装运机械组合优化方法[J]. 系统工程理论与实践, 2012, 32(10): 2262-2269. Wang Tingliang, Wang Li, Xia Guoping. Combinatorial optimization method for shipping machinery based on hybrid PSO[J]. Systems Engineering——Theory & Practice, 2012, 32(10): 2262-2269. [6] Van Den Bergh F, Engelbrecht A P. A study of particle swarm optimization particle trajectories[J]. Information Sciences, 2006, 176(8): 937-971. [7] Jiao B, Lian Z, Gu X. A dynamic inertia weight particle swarm optimization algorithm[J]. Chaos, Solitons and Fractals, 2008, 37(3): 698-705. [8] Yang X, Yuan J, Mao H. A modified particle swarm optimizer with dynamic adaptation[J]. Applied Mathematics and Computation, 2007, 189(2): 1205-1213. [9] Ratnaweera A, Halgamuge K S. Selforganizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 240-254. [10] Kennedy J. Bare bones particle swarms[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003: 80-87. [11] Omran M G H, Engelbrecht A, Salman A. Bare bones differential evolution[J]. European Journal of Operational Research, 2009, 196(1): 128-139. [12] Hou C H. Differential evolution bare bones particle swarm optimization and its application to image segmentation[C]//Proceedings of the 2011 Chinese Control and Decision Conference, 2011: 1680-1683. [13] 王攀攀, 史丽萍, 张勇, 等. 采用一种混合骨干微粒群优化算法的感应电机转子断条故障诊断[J]. 中国电机工程学报, 2012, 32(30): 73-81. Wang Panpan, Shi Liping, Zhang Yong, et al. Broken rotor bar fault diagnosis of induction motors using a hybrid bare-bones particle swarm optimization algorithm[J]. Proceedings of the CSEE, 2012, 32(30): 73-81. [14] Zhang Y, Gong D W, Ding Z H. A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch[J]. Information Sciences, 2012, 192: 212-227. [15] Adri B P, Pandu R G, Gabriel S H. Evaluation of stochastic global optimization methods for modeling vapor-liquid equilibrium data[J]. Fluid Phase Equilibria, 2010, 287(2): 111-125. [16] Lu Y L, Zhou J Z, Hui Q, et al. Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects[J]. Engineering Applications of Artificial Intelligence, 2011, 24(4): 378-387. [17] Engelbrecht A P. Fundamentals of computational swarm intelligence[M]. Wiley, 2006.
2015, Vol. 35 
