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Different implementations of bare bones particle swarm optimization |
ZHANG Zhen, PAN Zai-ping, PAN Xiao-hong |
Faculty of Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract 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.
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Published: 10 September 2015
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骨干粒子群算法两种不同实现的优化特性
总结了骨干粒子群算法(BBPSO)的一般形式, 指出决定BBPSO算法本质的4个要素. BBPSO在实施中, 粒子不同维度采用的随机变量值相同或不同, 这将导致算法的特性及适合的优化对象不同. 记相同的为I型实现, 不同的为II型实现, 通过实验指出2种实现的差别:I型实现有各向同性的优点, 但是粒子多样性差;II型粒子多样性更优, 但各向异性, 使用高斯、柯西、指数和均匀分布形式的II型BBPSO都倾向于沿坐标轴寻解. 从理论上分析了这些差别的成因, 指出I型实现总体性能较差, 只适合优化梯度变化明显的单峰函数; II型实现总体性能较好, 擅长求解峰的方向平行于坐标轴的单峰或多峰函数.
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