1. 重庆邮电大学计算机科学与技术研究所,重庆,400065
2. 安徽工业大学计算机科学学院,安徽,马鞍山,243002
3. 重庆邮电大学计算机科学与技术研究所重庆,400065
4. 安徽工业大学计算机科学学院安徽马鞍山,243002
纸质出版:2011
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申元霞, 王国胤, 曾传华. PSO模型种群多样性与学习参数的关系研究[J]. 电子学报, 2011,39(6):1238-1244.
SHEN Yuan-xia, WANG Guo-yin, ZENG Chuan-hua. Study on the Relationship Between Population Diversity and Learning Parameters in Particle Swarm Optimization[J]. Acta Electronica Sinica, 2011, 39(6): 1238-1244.
粒子群优化(Particle Swarm Optimization
PSO)算法在求解复杂多峰问题时
易陷入早期收敛.通过调整惯性权重和加速系数来控制种群多样性是缓解PSO早期收敛的一个重要手段
但是目前对惯性权重和加速系数的设置主要依赖于实验设计
缺乏必要的理论支撑.针对该问题
本文提出了一种以种群未来的多样性变化调整PSO算法参数的方法.该方法首先在种群当前状态已知的条件下计算种群下一时刻多样性的期望表达式
再采用多元函数极值理论的分析方法给出了惯性权重
加速系数与种群下一时刻多样性的数学关系
该结果为PSO学习参数控制种群多样性提供有力的数学理论依据.
PSO can easily suffer from the premature convergence when solving complex multimodal problems.It is an important method for relieving the premature convergence to control the population diversity by adjusting the inertia weight and acceleration coefficients.However
the setting of the inertia weight and acceleration coefficients is dependent on the design of experiments and lack of the support of the theory.To solve this problem
the new method is proposed in which the change of the prospective population diversity is used to adjust the setting of learning parameters in this paper.Firstly
the expression of the population diversity at the next time step is computed in the condition of the known current population state.Then the mathematic relationship between the inertia weight
acceleration coefficients and the population diversity at the next time step is presented by the extremum theory on multivariate function
which provides a theoretical foundation to control the population diversity by learning parameters.
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