Computer Science ›› 2017, Vol. 44 ›› Issue (4): 269-274.doi: 10.11896/j.issn.1002-137X.2017.04.056

Previous Articles     Next Articles

Application of Bacteria Foraging Algorithm in High-dimensional Optimization Problems

LI Jun and DANG Jian-wu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Excessive empirical parameters in the former self-adaptation step formula caused the defect in terms of failing to achieve self-adaptation in bacteria foraging optimization algorithm.Therefore,a revised step formula has been proposed,which enables step length to be relevant to the present evolution generation of individual bacteria as well as the optimal range of the problem to be solved,in order to achieve the step length self-adaption.Besides,the combination of chaotic thoughts and differential evolution thoughts with bacteria foraging algorithm can improve both the initial process and optimal process of the algorithm.This method increased the diversity of groups,preventing the algorithm from falling into the local optima due to the precocious.In the optimal process of high-dimensional problem,fractal dimension optimization is used to replace the former method.The fractal dimension optimization means that the information of every dimension will be updated one by one on the basis of whether the new position of every dimension changes comparing to the fitness value.Dealing with the problems in different dimensions can boost the precision and efficiency of the algorithm obviously.Experiments show that through the testing of multiple standard test functions in the hyperspace,the revised algorithm optimizing in the hyperspace has several benefits,such as fast speed,high precision and the simple process of solving.It has improved manifestly in terms of precision when comparing to other modified programs.

Key words: Bacteria foraging optimization algorithm(BFO),Adaptive step size,Chaos theory,Differential evolution(DE),High-dimensional optimization

[1] PASSINO K M.Biomimicry of bacterial foraging for distributed optimization and control[J].IEEE Control Systems Magazine,2002,22(3):52-67.
[2] ZHOU Y L.Research and application on bacteria foraging optimization algorithm[J].Computer Engineering and Applications,2010,6(20):16-21.(in Chinese) 周雅兰.细菌觅食优化算法的研究与应用[J].计算机工程与应用,2010,46(20):16-21.
[3] CHATZIS S P,KOUKAS S.Numerical optimization using sy-nergetic swarms of foraging bacterial populations[J].Expert Systems with Applications,2011,38(12):15332-15343.
[4] CHEN J C,HU G W,DU X Y.Adaptive variable step size bacterial foraging optimization[J].Commputer Engineering and Applications,2012,48(33):29-33.(in Chinese) 陈建超,胡桂武,杜小勇.自适应变步长菌群优化算法[J].计算机工程与应用,2012,8(33):29-33.
[5] DAS S,BISWAS A,DASGUPTA S,et al.Bacterial foraging optimization algorithm:theoretical foundations,analysis,and applications[J].Foundations of Computational Intelligence,2009,203:23-55.
[6] DASGUPTA S,DAS S,ABRAHAM A.Adaptive computational chemotaxis in bacterial foraging optimization:an analysis[J].IEEE Transactions on Evolutionary Compution,2009,3(4):919-941.
[7] XU X.Research on the Bacterial Foraging Optimization Algorithm[D].Jilin:Jilin University,2012.(in Chinese) 许鑫.细菌觅食优化算法研究[D].吉林:吉林大学,2012.
[8] VERMA P O,HANMANDLU M,KUMAR P,et al.A novel bacterial foraging technique for edge detection[J].Pattern Re-cognition Letters,2011,32(8):1187-1196
[9] ZHANG G Y,WU Y G,TAN Y X.Bacterial Foraging Optimization Algorithm with Quantum Behavior[J].Journal of Electronics & Information Technology,2013,5(3):614-621.(in Chinese) 章国勇,伍永刚,谭宇翔.一种具有量子行为的细菌觅食优化算法[J].电子与信息学报,2013,5(3):614-621.
[10] LIU X L,ZHAO K L.Bacteria foraging optimization algorithm based on immune algorithm[J].Journal of Computer Applications,2012,32(3):634-637,653.(in Chinese) 刘小龙,赵奎领.基于免疫算法的细菌觅食优化算法[J].计算机应用,2012,32(3):634-637,653.
[11] SABER A Y.Economic dispatch using particle swarm optimization with bacterial foraging effect [J].Electrical Power and Ener-gy Systems,2012,34(1):38-46.
[12] LI J,DANG J W,BU F.Study on Adaptive Step Length Bacte-rial Foraging Algorithm[J].Journal of Lanzhou Jiaotong University,2013,2(6):10-14.(in Chinese) 李珺,党建武,卜锋.自适应步长的细菌觅食优化算法研究[J].兰州交通大学学报,2013,2(6):10-14.
[13] CHEN G,WU X D,ZHU X Q,et al.Efficient string matching with wildcards and length constraints [J].Knowledge and Information Systems,2006,10(4):399-419.
[14] NAVARRO G.Regular expression searching over Ziv-Lempelcompressed text[C]∥ Proceedings of the 12th Annual Sympo-sium on Combinatorial Pattern Matching.Berlin:Springer,2001:1-17.
[15] STORN R,PDEE K.Differential Evolution-A Simple and Efficient Heuristic for Global optimization over Continuous Spaces [J].Journal of Global Optimization,1997,11(4):341-359.
[16] LI J,DANG J W.Bacterial Foraging Algorithm for SolvingHigh-Dimensional Optimization Problems[J].Application research of Computers,2016,3(4):1024-1027,3.(in Chinese) 李珺,党建武.细菌觅食算法求解高维优化问题[J].计算机应用研究,2016,3(4):1024-1027,3.
[17] LI J,DANG J W,BU F.Reseach and Improve on Bacteria Foraging Optimization Algorithm[J].Computer Simulation,2013,30(4):344-347,415.(in Chinese) 李珺,党建武,卜锋.细菌觅食优化算法的研究与改进[J].计算机仿真,2013,0(4):344-347,5.
[18] CEC2005[DB/OL].http://www3.ntu.edu.sg/home/EPNSugan/index_files/ CEC-05/CEC05.htm.
[19] YAO X,LIU Y,LIN G.Evolutionary programming made faster [J].IEEE Transactions on Evolutionary Computation,1999,3(2):82-102.
[20] RATNAWEERA A,HALGAMUGE K S.Self organizing hie-rarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Trans on Evol Comput,2004,8(3):240-254.
[21] BISWAS A,DASGUPTA S,DAS S,et al.Synergy of PSO and bacterial foraging optimization:a comparative study on numerical benchmarks[C]∥Innovations in Hybrid Intelligent Systems.2007:255-263.
[22] KRINK T,VESTERSTROM J S,RIGET J.Particle swarm optimization with spatial particle extension[C]∥Proceedings of IEEE Congress on Evolutionary Computation.Honolulu,Hawaii USA,2002:1474-1497.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!