Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

doi:10.11772/j.issn.1001-9081.2014.10.2886

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2886-2890.DOI: 10.11772/j.issn.1001-9081.2014.10.2886

• Artificial intelligence • Previous Articles Next Articles

Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

WANG Peichong¹,²,GAO Wenchao²,³,QIAN Xu²,GOU Haiyan⁴,WANG Shenwen¹

1. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
2. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China;
3. Department of Computer, Tsinghua University, Beijing 100084, China;
4. HuaXin College, Shijiazhuang University of Economics, Shijiazhuang Hebei 050071, China

Received:2014-05-04 Revised:2014-06-16 Online:2014-10-01 Published:2014-10-30
Contact: WANG Peichong

应用精英反向学习的混合烟花爆炸优化算法

王培崇¹,²,高文超²,³,钱旭²,苟海燕⁴,汪慎文⁵

1. 石家庄经济学院信息工程学院，石家庄 050031；
2. 中国矿业大学(北京) 机电与信息工程学院，北京 100083；
3. 清华大学计算机系，北京 100084；
4. 石家庄经济学院华信学院，石家庄 050071
5. 石家庄经济学院信息工程学院，石家庄 050031

通讯作者: 王培崇
作者简介:王培崇（1972-），男，河北辛集人，副教授，博士，主要研究方向：计算智能、机器学习；
高文超（1986-），女，山东泰安人，讲师，博士，主要研究方向：模式识别；
钱旭（1962-），男，江苏南京人，教授，博士生导师，博士，主要研究方向：信息融合；
苟海燕（1984-），女，陕西西安人，硕士，主要研究方向：计算智能；
汪慎文（1978-），男，湖北武汉人，副教授，博士，主要研究方向：计算智能。
基金资助:
教育部博士点建设基金资助项目;河北省科技支撑计划项目;河北省教育厅基金资助项目;石家庄经济学院基金预研项目;博士科研基金资助项目;河北省青年拔尖人才支持计划项目

Abstract

Abstract:

Concerning the problem that Fireworks Explosion Optimization (FEO) algorithm is easy to be premature and has low solution precision, an elite Opposition-Based Learning (OBL) was proposed. In every iteration, OBL was executed by the current best individual to generate an opposition search populations in its dynamic search boundaries, thus the search space of the algorithm was guided to approximate the optimum space. This mechanism is helpful to improve the balance and exploring ability of the FEO. For keeping the diversity of population, the sudden jump probability of the individual to the current best individual was calculated, and based on it, the roulette mechanism was adopted to choose the individual which entered into the child population. The experimental simulation on five classical benchmark functions show that, compared with the related algorithm, the improved algorithm has higher convergence rate and accuracy for numerical optimization, and it is suitable to solve the high dimensional optimization problem.

摘要：

针对烟花爆炸优化(FEO)算法容易早熟、解精度低的弱点，提出了一种精英反向学习(OBL)的解空间搜索策略。在每次迭代过程中均对当前最佳个体执行反向学习，生成其动态搜索边界内的反向搜索种群，引导算法向包含全局最优的解空间逼近，以提高算法的平衡和探索能力。为了保持种群的多样性，计算种群内个体对当前最佳个体的突跳概率，并依据此概率值采用轮盘赌机制选择进入子种群的个体。通过在5组标准测试函数的实验仿真并与相关的算法对比，结果表明所提出的改进算法对数值优化具有更高的收敛速度和收敛精度，适合求解高维的数值优化问题。

CLC Number:

TP18

WANG Peichong GAO Wenchao QIAN Xu GOU Haiyan WANG Shenwen. Hybrid fireworks explosion optimization algorithm using elite opposition-based learning[J]. Journal of Computer Applications, 2014, 34(10): 2886-2890.

王培崇高文超钱旭苟海燕汪慎文. 应用精英反向学习的混合烟花爆炸优化算法[J]. 计算机应用, 2014, 34(10): 2886-2890.

References

［1］ABDELKADER R F. An improved discrete PSO with GA operators for efficient QoS-multicast routing ［J］. International Journal of Hybrid Information Technology, 2011,4(2):23-38.
［2］SIVANANAITHAPERUMAL S, AMALL S M J, BASKAR S, et al.Constrained self-adaptive differential evolution based design of robust optimal fixed structure controller ［J］. Engineering Applications of Artificial Intelligence, 2011,24(6):1084-1093.［3］ZHOU Y, HUANG Z, LIU H. Discrete glowworm swarm optimization algorithm for TSP problem ［J］. Acta Electronica Sinica, 2012,40(6):1164-1170.( 周永权,黄正新,刘洪霞.求解TSP 问题的离散型萤火虫群优化算法［J］.电子学报,2012,40(6):1164-1170.)
［4］WU H, CHEN X, MAO Q, et al.Improved ant colony algorithm based on natural selection strategy for solving TSP problem ［J］. Journal on Communications, 2013,4(4):165-170. (吴华锋,陈信强,毛奇凰，等.基于自然选择策略的蚁群算法求解 TSP 问题［J］.通信学报,2013,4(4):165-170.)
［5］TAN Y, ZHU Y. Fireworks algorithms for optimization ［C］// Proceedings of International Conference on Swarm Intelligence. Piscataway: IEEE Press, 2010:355-364.
［6］DU Z. Study on improvement of explosion radius in fireworks algorithm ［J］. Computer Era, 2013,10(1):28-29.(杜振鑫.烟花算法中爆炸半径的改进研究［J］.计算机时代,2013,10(1):28-29.)
［7］CAO J, JIA H, LI T. A fireworks explosion optimization algorithm ［J］. Computer Engineering and Science, 2011,33(1):138-142.(曹炬,贾红,李婷婷.烟花爆炸优化算法［J］.计算机工程与科学,2011,33(1):138-142.)
［8］CAO J, JI Y. An improved fireworks explosion optimization algorithm and its convergence analysis ［J］. Computer Engineering, 2012,34(1):90-93.(曹炬,季艳芳.改进的烟火爆炸优化算法及其收敛性分析［J］.计算机工程与科学,2012,34(1):90-93.)
［9］CAO J, LI T, JIA H. Fireworks explosion optimization algorithm with genetic operators ［J］. Computer Engineering, 2010,36(12):149-151.(曹炬,李婷婷,贾红.带有遗传算子的烟花爆炸优化算法［J］.计算机工程,2010,36(12):149-151.)
［10］TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence ［C］// Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligence Agent, Web Technologies and Internet Commerce. Piscataway: IEEE Press, 2005:695-701.

［11］Al-QUNAIEER F S, TIZHOOSH H R, RAHNAMAYAN S. Opposition based computing—a survey ［C］// Proceedings of the 2010 International Joint Conference on Neural Networks. Piscataway: IEEE Press, 2010:1-7.

［12］WANG H. Research on intelligence algorithms based on region transformation search and its applications ［D］. Wuhan: Wuhan University, 2011.(王晖.区域变换搜索的智能算法研究［D］.武汉:武汉大学,2011.)

[1]	. Semi-supervised representation learning method combining graph autoencoder and clustering [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. Partially explainable non-negative matrix tri-factorization based on prior knowledge [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Hybrid ITÖ algorithm for multi-scale colored traveling salesman problem [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Stock trend prediction method based on temporal hypergraph convolutional neural networks [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Analysis of complex spam filtering algorithm based on neural network [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	. Doubly feature weighted fuzzy support vector machine [J]. Journal of Computer Applications, 0, (): 0-0.
[7]	. Adaptive artificial fish swarm algorithm utilizing gene exchange [J]. Journal of Computer Applications, 0, (): 0-0.
[8]	. Online kernel regression based on random sketching method [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Hierarchical classification online streaming feature selection algorithm based on ReliefF algorithm [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Students' facial expression recognition and intelligent teaching evaluation in classroom teaching videos based on deep attention network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Multi-person classroom action recognition in classroom teaching videos based on deep spatiotemporal residual convolution neural network [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	. Efficient attribute reduction algorithm based on local conditional discernibility [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	. Variable precision neighborhood equivalent granular neighborhood decision tree construction algorithm [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Genetic algorithm for approximate concept and its recommendation application [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Recommendation model for user attribute preference modeling based on convolutional neural networks interaction [J]. Journal of Computer Applications, 0, (): 0-0.

Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

应用精英反向学习的混合烟花爆炸优化算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics