基于反向学习与机动爆炸烟花优化算法

李席广; 韩守飞; 拱长青

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基于反向学习与机动爆炸烟花优化算法

更新时间：2026-09-04

- 基于反向学习与机动爆炸烟花优化算法
- Fireworks Algorithm Based on Reverse Learning and Maneuver Explode
- 微电子学与计算机 2017年34卷第7期页码：105-112
- 作者机构：
  
  沈阳航空航天大学机构计算机学院,辽宁,沈阳,110136
- 作者简介：
- 基金信息：
  
  辽宁省教育厅科学基金 L2013064中航工业技术创新基金(基础研究类) 2013S60109R
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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李席广, 韩守飞, 拱长青. 基于反向学习与机动爆炸烟花优化算法[J]. 微电子学与计算机, 2017, 34(7): 105-112.

LI Xi-guang, HAN Shou-fei, GONG Chang-qing. Fireworks Algorithm Based on Reverse Learning and Maneuver Explode[J]. 2017, 34(7): 105-112.
李席广, 韩守飞, 拱长青. 基于反向学习与机动爆炸烟花优化算法[J]. 微电子学与计算机, 2017, 34(7): 105-112. DOI：

LI Xi-guang, HAN Shou-fei, GONG Chang-qing. Fireworks Algorithm Based on Reverse Learning and Maneuver Explode[J]. 2017, 34(7): 105-112. DOI：

摘要

针对烟花算法(Fireworks Algorithm

FWA)性能提升瓶颈和收敛速度较慢的问题

通过引入反向学习策略和机动爆炸的机制

提出了基于反向学习与机动爆炸烟花优化算法(Fireworks Algorithm based on Reverse learning and Maneuver explode

RLMEFWA).该算法首先采用反向学习策略取代随机初始化生成初始种群以保证群体的多样性; 然后每个烟花根据其在当前群体中的位置的优劣情况来选择不同爆炸的方式

处于较优位置的烟花选择机动爆炸方式

以当前种群最优位置为基准

改变自身位置信息向其靠近; 处于较劣位置的烟花选择非机动爆炸方式

随机改变自身的位置信息.分别把烟花算法(FWA)、标准粒子群算法(SPSO)、增强烟花算法(EFWA)和RLMEFWA在10个典型的基准测试函数中进行仿真对比

结果表明在收敛速度和计算精度以及稳定性方面RLMEFWA均优于其他三种算法.

Abstract

Aiming at the performance bottlenecks and slow convergence of fireworks algorithm (Fireworks Algorithm

FWA)

By putting reverse learning and maneuver explode into FWA

this paper proposes fireworks algorithm based on reverse learning and maneuver explode (Fireworks Algorithm based on Reverse learning and Maneuver explode

RLMEFWA). In the algorithm

reverse learning strategy was introduced to generate initial population

which strengthened the diversity of population

then each firework applies the position in the current group to choose a different explode way. Fireworks in the better position to select maneuvering explosion mode whose random orbit closer to the optimum position. Fireworks in an inferior position explode select non-motorized mode with random orbit. In our simulation

we compare FWA

SPSO

EFWA and RLMEFWA with 10 typical benchmark functions. The results show that RLMEFWA is better than FWA

SPSO

and EFWA in terms of convergence speed and accuracy and stability.

关键词

Keywords

references

ZHENG S, JANECEK A, LI J, et al. Dynamic search in fireworks algorithm[C]//2014 IEEE Congress on Evolutionary Computation. Beijing, China, 2014: 3222-3229.

PEI Y, ZHENG S, TAN Y, et al. An empirical study on influence of approximation[C]//IEEE International Conference on Systems, Man, and Cybernetics. Seoul, South Korea, 2012: 1322-1327.

LI J, ZHENG S, TAN Y. Adaptive Fireworks Algorithm[C]//2014 IEEE Congress on Evolutionary Computation. Beijing, China, 2014: 3214-3221.

Zheng S, Janecek A, Tan Y. Enhanced fireworks algorithm[C]// Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE, 2013.

谭营, 郑少秋. 烟花算法研究进展[J]. 智能系统学报, 2014, 9(5): 515-528. DOI: 515

谭营. 烟花算法引论[M]. 北京: 科学出版社, 2015.

Tizhoosh H. Opposition-based learn: A new scheme for machine intelligence[C]//Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation. Vienna, Austria: IEEE, 2005: 695-701.

Bratton D, Kennedy J. Defining a standard for particle swarm optimization[C]//Swarm Intelligence Symposium, 2007. IEEE, 2007.

LIU J, ZHENG S, TAN Y. The improvement on controlling exploration and exploitation of fireworks algorithm[M] //Advances in Swarm Intelligence. Berlin: Springer, 2013: 11-23.

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