Gao Yang, Du Wenbo, Yan Gang
School of Electronic and Information Engineering, Beihang University, Beijing 100191, People's Republic of China.
Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 USA.
Sci Rep. 2015 Mar 19;5:9295. doi: 10.1038/srep09295.
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
粒子群优化算法（PSO）是一种受自然启发的算法，在解决许多实际问题中表现出卓越性能。在原始的PSO及其大多数变体中，所有粒子都被平等对待，忽略了结构异质性对个体行为的影响。在此，我们采用复杂网络来表示群体的种群结构，并提出一种选择性信息粒子群优化算法（SIPSO），其中粒子根据其连接情况选择不同的学习策略：高度连接的中心粒子从其所有邻居获取完整信息，而连接较少的非中心粒子只能跟随单个表现最佳的邻居。在广泛使用的基准函数上进行的大量数值实验表明，我们的SIPSO算法在成功率、解的质量和收敛速度方面显著优于PSO及其现有变体。我们还从微观角度探索了进化过程，从而发现了粒子在优化中所起的不同作用。中心粒子将优化过程导向正确方向，而非中心粒子维持必要的种群多样性，从而使SIPSO获得最佳整体性能。这些发现加深了我们对群体智能的理解，并可能揭示自然群体和聚集行为中信息交换的潜在机制。
