Enhanced fireworks algorithm with adaptive merging strategy and guidance operator

doi:10.11772/j.issn.1001-9081.2020060887

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 81-86.DOI: 10.11772/j.issn.1001-9081.2020060887

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles Next Articles

Enhanced fireworks algorithm with adaptive merging strategy and guidance operator

LI Kewen¹, MA Xiangbo¹, HOU Wenyan²

1. College of Computer Science and Technology, China University of Petroleum, Qingdao Shandong 266580, China;
2. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao Shandong 266580, China

Received:2020-05-31 Revised:2020-08-12 Online:2020-11-12 Published:2021-01-10
Supported by:
This work is partially supported by the Major Program of the National Natural Science Foundation of China (51991365).

带有自适应合并策略和导向算子的增强型烟花算法

李克文¹, 马祥博¹, 候文艳²

1. 中国石油大学(华东) 计算机科学与技术学院, 山东青岛 266580;
2. 中国石油大学(华东) 海洋与空间信息学院, 山东青岛 266580

通讯作者: 马祥博
作者简介:李克文(1969-),男,黑龙江齐齐哈尔人,教授,博士生导师,博士,CCF高级会员,主要研究方向:人工智能、软件工程、机器学习、数据挖掘;马祥博(1995-),男,山东潍坊人,硕士研究生,主要研究方向:群智能优化、数据挖掘、大数据分析;候文艳(1995-),女,云南大理人,硕士研究生,主要研究方向:群智能优化、数据挖掘、行为识别。
基金资助:
国家自然科学基金重大项目（51991365）。

Abstract

Abstract: In order to overcome the shortcomings of traditional FireWorks Algorithm (FWA) in the process of optimization, such as the search range limited by explosion radius and the lack of effective interaction between particles, an Enhanced FireWork Algorithm with adaptive Merging strategy and Guidance operator (EFWA-GM) was proposed. Firstly, according to the position relationship between fireworks particles, the overlapping explosion ranges in the optimization space were adaptively merged. Secondly, by making full use of the position information of high-quality particles through layering the spark particles, the guiding operator was designed to guide the evolution of suboptimal particles, so as to improve the accuracy and convergence speed of the algorithm. Experimental results on 12 benchmark functions show that compared with Standard Particle Swarm Optimization (SPSO) algorithm, Enhanced FireWorks Algorithm (EFWA), Adaptive FireWorks Algorithm (AFWA), dynamic FireWorks Algorithm (dynFWA), and Guided FireWorks Algorithm (GFWA), the proposed EFWA-GM has better optimization performance in optimization accuracy and convergence speed, and obtains optimal solution accuracy on 9 benchmark functions.

Key words: swarm intelligence algorithm, FireWorks Algorithm (FWA), guidance operator, adaptive merging strategy, Adaptive FireWork Algorithm (AFWA)

摘要： 针对传统烟花算法（FWA）在寻优过程中爆炸半径限制搜索范围、粒子间缺少有效交互的缺点，提出带有自适应合并策略和导向算子的增强型烟花算法（EFWA-GM）。首先根据烟花粒子间的位置关系，对寻优空间中重叠的爆炸范围进行自适应合并；其次通过对火花粒子进行分层来充分利用优质粒子的位置信息，从而设计导向算子引导次优粒子进化，以提高算法的寻优精度和收敛速度。在12个标准测试函数上的实验结果表明，所提出的EFWA-GM相较于标准粒子群（SPSO）算法、增强型烟花算法（EFWA）、自适应烟花算法（AFWA）、动态烟花算法（dynFWA）、有导烟花算法（GFWA）在寻优精度和收敛速度方面具有更好的优化性能，并在9个测试函数上取得最优的求解精度。

关键词: 群智能算法, 烟花算法, 导向算子, 自适应合并策略, 自适应烟花算法

CLC Number:

TP301

LI Kewen, MA Xiangbo, HOU Wenyan. Enhanced fireworks algorithm with adaptive merging strategy and guidance operator[J]. Journal of Computer Applications, 2021, 41(1): 81-86.

李克文, 马祥博, 候文艳. 带有自适应合并策略和导向算子的增强型烟花算法[J]. 计算机应用, 2021, 41(1): 81-86.

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Enhanced fireworks algorithm with adaptive merging strategy and guidance operator

带有自适应合并策略和导向算子的增强型烟花算法

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