随机漂移粒子群算法
Random Drift Particle Swarm Optimization
摘要：
本文从外电场中金属导体中的自由电子模型出发，提出了随机漂移粒子群优化算法（RDPSO），并对其进行了系统的分析和实证研究。自由电子模型认为电子在置于外电场中的导体中既有热运动又有漂移运动。首先描述了RDPSO算法的动机，并通过模拟电子的热运动和漂移运动设计了粒子的速度方程，这两种运动都将电子引导到外电场中势能最小的位置。然后，对该算法进行了全面的分析，以便深入了解RDPSO算法的工作原理。对RDPSO算法中单个粒子的随机动力学行为进行了理论分析和仿真。算法本身的搜索行为也进行了详细研究，通过分析粒子之间的相互作用。通过将不同的随机速度分量与不同的邻域拓扑结构，提出了RDPSO算法的一些变体。最后，使用CEC2005基准测试集的一组基准测试函数对RDPSO算法进行了实证研究。在对粒子行为进行理论分析的基础上，采用两种方法控制算法参数，并对参数值的选取进行了实验分析，以使RDPSO算法及其变体在实际应用中具有良好的整体性能。进一步将RDPSO算法与PSO的其他变体进行性能比较，证明了RDPSO算法的有效性。
英文摘要：
The random drift particle swarm optimization (RDPSO) algorithm, inspired by the free electron model in metal conductors placed in an external electric field, is presented, systematically analyzed and empirically studied in this paper. The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field. The motivation of the RDPSO algorithm is described first, and the velocity equation of the particle is designed by simulating the thermal motion as well as the drift motion of the electrons, both of which lead the electrons to a location with minimum potential energy in the external electric field. Then, a comprehensive analysis of the algorithm is made, in order to provide a deep insight into how the RDPSO algorithm works. It involves a theoretical analysis and the simulation of the stochastic dynamical behavior of a single particle in the RDPSO algorithm. The search behavior of the algorithm itself is also investigated in detail, by analyzing the interaction between the particles. Some variants of the RDPSO algorithm are proposed by incorporating different random velocity components with different neighborhood topologies. Finally, empirical studies on the RDPSO algorithm are performed by using a set of benchmark functions from the CEC2005 benchmark suite. Based on the theoretical analysis of the particle's behavior, two methods of controlling the algorithmic parameters are employed, followed by an experimental analysis on how to select the parameter values, in order to obtain a good overall performance of the RDPSO algorithm and its variants in real-world applications. A further performance comparison between the RDPSO algorithms and other variants of PSO is made to prove the efficiency of the RDPSO algorithms.
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