A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy

doi:10.1109/ACCESS.2017.2776958

2018-01-01

DOI：10.1109/ACCESS.2017.2776958

原文链接

查看原文

摘要

En 中文

Brain storm optimization (BSO) is a young and promising population -based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies, such as K -means clustering, resulting in large computational burdens. This paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduce redundancy in the pattern for generating individuals. Third, a quantum -behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum -behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, particle swarm optimization, and differential evolution on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms.

Keyword:

Global optimization

brain storm optimization (BSO)

periodic quantum learning strategy

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Access

IF:

3.6

论文数:

9.8W

被引数:

29.4W

机构

Nanchang University

学者数:

3.7W

论文数: 2.1W

被引数: 3.7W

引用论文

Optimum D-STATCOM placement using firefly algorithm for power quality enhancement

2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO)

IF0

2013-06-01

PREAI

Masoud Farhoodnea; Azah Mohamed; Hussain Shareef; Hadi Zayandehroodi

Pulmonary artery pressure and iron deficiency in patients with upregulation of hypoxia sensing due to homozygous VHLR200W mutation (Chuvash polycythemia)

Haematologica

IF0

2011-10-11

OAAI

C. A. Sable; Z. Y. Aliyu; N. Dham; M. Nouraie; V. Sachdev; S. Sidenko; G. Y. Miasnikova; L. A. Polyakova; A. I. Sergueeva; D. J. Okhotin; V. Bushuev; A. T. Remaley; X. Niu; O. L. Castro; M. T. Gladwin; G. J. Kato; J. T. Prchal; V. R. Gordeuk

Facile synthesis of α-MnO2 nanorods at low temperature and their microwave absorption properties

Materials Chemistry and Physics

IF0

2014-02-01

PREAI

Hongtao Guan; Jianbin Xie; Gang Chen; Yude Wang

Robust unobtrusive fall detection using infrared array sensors

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

IF0

2017-11-01

OAAI

Xiuyi Fan; Huiguo Zhang; Cyril Leung; Zhiqi Shen