A general bare bones particle swarm optimization (BBPSO) form was presented, which consists of four key elements. In the implementation of BBPSO, whether the different dimensions of a particle use the same random variable or not conduct to two different algorithms. Denote the former as BBPSO-I, and the latter as BBPSO-II. Experimental results indicate that BBPSO-I is a rotational invariant algorithm with poor swarm diversity, while BBPSO-II is rotational variant with better swarm diversity and general performance. The using of Gaussian, Cauchy, Exponential or Uniform distribution makes particles of BBPSO-II tend to move along the axes. These features were clarified by theoretical analysis. Some advice on the application of BBPSO was given. BBPSO-I is suitable for unimodal functions with obvious gradient descent, while BBPSO-II obtains generally better performance, especially on optimizing functions with peaks along axes．
ZHANG Zhen, PAN Zai-ping, PAN Xiao-hong. Different implementations of bare bones particle swarm optimization. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(7): 1350-1357.
［1］ KENNEDY J. Bare bones particle swarms ［C］∥ Proceedings of the Swarm Intelligence Symposium. Indiana: IEEE,2003: 80-87．
［2］ SUN J， XU W B, FENG B. A global search strategy of quantum-behaved particle swarm optimization ［C］∥ Conference on Cybernetics and Intelligent Systems. Singapore: IEEE, 2004: 111-116．
［3］ SUN J, FANG W, WU X, et al. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection ［J］. Evolutionary Computation, 2012, 20(3): 349-393．
［4］ FANG W, SUN J, DING Y, et al. A review of quantum-behaved particle swarm optimization ［J］. IETE Technical Review, 2010, 27(4): 336-348．
［5］ ZHANG H, FERNNDEZ-VARGAS J A, RANGAIAH G P, et al. Evaluation of integrated differential evolution and unified bare-bones particle swarm optimization for phase equilibrium and stability problems ［J］. Fluid Phase Equilibria, 2011, 310(1): 129-141．
［6］ YAO J, HAN D. Improved barebones particle swarm optimization with neighborhood search and its application on ship design ［J］. Mathematical Problems in Engineering, 2013, 2013(1): 113．
［7］ RICHER T J, BLACKWELL T. The Lévy particle swarm ［C］∥ IEEE Congress on Evolutionary Computation. Vancouver: IEEE, 2006: 808-815．
［8］ AL-RIFAIE M M, BLACKWELL T. Bare bones particle swarms with jumps ［C］∥Algorithmic Number Theory Symposium.Brussels:Springer, 2012: 49-60．
［9］ BLACKWELL T. A study of collapse in bare bones particle swarm optimization ［J］. IEEE Transactions on Evolutionary Computation, 2012, 16(3): 354-372．
［10］ HSIEH H, LEE T. A modified algorithm of bare bones particle swarm optimization ［J］. International Journal of Computer Science Issues, 2010, 7(6): 12-17．
［11］ LI Y, XIANG R, JIAO L, et al. An improved cooperative quantum-behaved particle swarm optimization ［J］. Soft Computing, 2012, 16(6): 1061-1069．
［12］ ZHANG Y, GONG D, SUN X, et al. Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis ［J］. Soft Computing, 2013, 18(7): 116．
［13］ ZHANG Y, GONG D, GENG N, et al. Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects ［J］. Applied Soft Computing, 2014, 5(18): 248-260．
［14］ 史丽萍,王攀攀,胡泳军,等. 基于骨干微粒群算法和支持向量机的电机转子断条故障诊断［J］. 电工技术学报, 2014, 29(1): 147-155．
SHI Li-ping, WANG Pan-pan, HU Yong-jun, et al. Broken rotor bar fault diagnosis of induction motors based on bare-bone particle swarm optimization ［J］. Transactions of China Electricotechnical Society, 2014, 29(1): 147-155．
［15］ CAMPOS M, KROHLING R A. Hierarchical bare bones particle swarm for solving constrained optimization problems ［C］∥ IEEE Congress on Evolutionary Computation. Cancun: IEEE, 2013: 805-812．
［16］ CHANG Y, CHUEH C, XU Y, et al. Bare bones particle swarm optimization with considering more local best particles ［C］∥ International Symposium on Instrumentation and Measurement, Sensor Network and Automation. Toronto : IEEE, 2013: 1105-1108．
［17］ SPEARS W M, GREEN D T, SPEARS D F. Biases in particle swarm optimization ［J］. International Journal of Swarm Intelligence Research, 2010, 1(2): 34-57．
［18］ WOLPERT D H, MACREADY W G. No free lunch theorems for optimization ［J］. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82.
