基于冯诺依曼拓扑结构的骨干粒子群优化算法

计算机工程与科学

基于冯诺依曼拓扑结构的骨干粒子群优化算法

王明慧，戴月明，田娜，王艳

（江南大学物联网工程学院,江苏无锡 214122)

收稿日期:2015-12-07 修回日期:2016-04-12 出版日期:2017-08-25 发布日期:2017-08-25
基金资助:
国家863计划(2014AA041505);国家自然科学基金(61572238)

Bare bones particle swarm optimization

based on Von Neumann topology

WANG Ming-hui，DAI Yue-ming，TIAN Na，WANG Yan

（School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)

Received:2015-12-07 Revised:2016-04-12 Online:2017-08-25 Published:2017-08-25

摘要/Abstract

摘要：

为了改善骨干粒子群优化BBPSO算法的易早熟、易陷入局部最优解等缺点，提出了一种基于冯诺依曼拓扑结构的改进骨干粒子群优化VBBPSO算法。新算法提出“兼顾落后粒子”概念，通过应用冯诺依曼拓扑结构构造邻域，用邻域最优解取代全局最优解，引入中心项调节系数，在邻域范围内调整BBPSO算法的进化中心项与离散控制项，提高了算法全局探索能力与局部开发能力。实验结果表明，较几种经典的BBPSO算法，VBBPSO算法的综合性能有明显提升。

关键词: 骨干粒子群优化算法, 冯诺依曼拓扑结构, 中心项调节系数, 落后粒子

Abstract:

To address the shortages such as premature convergence and easily falling into local optimum of the bare bone particle swarm algorithm (BBPSO), we propose an improved BBPSO based on Von Neumann topology (VBBPSO). The new algorithm advocates taking lagging particles into account, constructs neighboring areas by applying the Von Neumann topology, replaces the global optimal solution with the neighboring optimal solution, introduces the central adjustment coefficient, and adjusts the evolutional central term and the discrete control item of the BBPSO algorithm in neighboring areas, thus improving the global exploration ability and the local development ability. Experimental results indicate that the performance of the VBBPSO has a better performance in comparison with other classical algorithms.

Key words: bare bones PSO, Von Neumann topology, center adjustment coefficient, lagging particles

王明慧，戴月明，田娜，王艳. 基于冯诺依曼拓扑结构的骨干粒子群优化算法[J]. 计算机工程与科学.

WANG Ming-hui，DAI Yue-ming，TIAN Na，WANG Yan .

Bare bones particle swarm optimization

based on Von Neumann topology

[J]. Computer Engineering & Science.