Skip to main content

Advertisement

Springer Nature Link
Log in

An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper puts forward an improved multi-objective bacterial colony chemotaxis (MOBCC) algorithm based on Pareto dominance. A time-varying step size tactic is adopted to increase the global and local searching abilities of the improved MOBCC algorithm. An external archive is created to keep previously found Pareto optimal solutions. A non-dominated sorting method integrating crowding distance assignment is applied to enhance the time efficiency of the improved MOBCC algorithm. A hybrid method combining bacterial individual mutation, oriented mutation of bacterial colony and local search of external archive is applied to enhance the convergence of the algorithm and maintain the diversity of solution set. The framework of MOEAs based on Pareto dominance is integrated into the improved MOBCC algorithm properly through replacements of the bacterial individuals in the bacterial colony, archive operation, and updating of the bacterial colony. The improved MOBCC algorithm is compared with three common multi-objective optimization algorithms SPEA2, NSGA-II and MOEA/D on fifteen test problems and evolution of optimization, and the experimental results confirm the validity of the improved MOBCC algorithm. Furthermore, the effects of the improved MOBCC algorithm’s parameters on the performance of the improved MOBCC algorithm are analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adler J (1966) Chemotaxis in bacteria. Science 153:708–716

    Article  Google Scholar 

  • Binh TT, Korn U (1989) MOBES: a multiobjective evolution strategy for constrained optimization problems. In: Proceedings of the third international conference on genetic algorithms, 176–182

  • Biswas PP, Suganthan PN, Mallipeddi R et al (2019) Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms. Soft Comput 4

  • Bremermann HJ (1974) Chemotaxis and optimization. J Franklin Inst 297:397–404

    Article  Google Scholar 

  • Chankong V, Haimes YY (1983) Multiobjective decision making: theory and methodology. Dover, Amsterdam

    MATH  Google Scholar 

  • Cheng H, Lu Z, Sun S (2011) Multiobjective optimization using bacterial colony chemotaxis. In: Proceedings of 2011 IEEE international conference on intelligent computing and intelligent systems, vol 1, pp 27–33

  • Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation, vol 2, pp 1051–1056

  • Coello CAC, Lamont GB, Veldhuizen DAV (2007) Evolutionary algorithms for solving multi-objective problems. Departamento de Computación. https://www.cs.cinvestav.mx/~emoobook. Accessed 1 May 2008

  • Czyzżak P, Jaszkiewicz A (1998) Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7(1):34–47

    Article  Google Scholar 

  • Deb K, Agrawal RB (2000) Simulated binary crossover for continuous search space. Complex Syst 9(3):115–148

    MathSciNet  MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal S et al (2002a) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Deb K, Thiele L, Laumanns M et al (2002b) Scalable multi-objective optimization test problems. In: Proceedings of the 2002 congress on evolutionary computation, vol 1, pp 825–830

  • Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1):1–16

    Article  Google Scholar 

  • Guo W, Chen M, Wang L et al (2017) Hyper multi-objective evolutionary algorithm for multi-objective optimization problems. Soft Comput 21(20):5883–5891

    Article  Google Scholar 

  • Guzmán MA, Delgado A, Carvalho JD (2010) A novel multiobjective optimization algorithm based on bacterial chemotaxis. Eng Appl Artif Intell 23:292–301

    Article  Google Scholar 

  • Hiwa S, Nishioka M, Hiroyasu T et al (2015) Novel search scheme for multi-objective evolutionary algorithms to obtain well-approximated and widely spread Pareto solutions. Swarm Evol Comput 22:30–46

    Article  Google Scholar 

  • Knowles J, Corne D (1999) The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation. In: Proceedings of the 1999 congress on evolutionary computation, vol 1, pp 98–105

  • Kursawe F (1990) A variant of evolution strategies for vector optimization. In: International conference on parallel problem solving from nature. Springer, Berlin

  • Li W, Wang H, Zou Z et al (2005) Function optimization method based on bacterial colony chemotaxis. J Circuits Syst 10(1):58–63

    Google Scholar 

  • Lin Y, Du W (2019) Multi-objective differential evolution with dynamic hybrid constraint handling mechanism. Soft Comput 23:4341–4355

    Article  Google Scholar 

  • Lu Z, Feng T, Li X (2013) Low-carbon emission/economic power dispatch using the multi-objective bacterial colony chemotaxis optimization algorithm considering carbon capture power plant. Electr Power Energy Syst 53:106–112

    Article  Google Scholar 

  • Lu Z, Zhao H, Xiao H et al (2015) An improved multi-objective bacteria colony chemotaxis algorithm and convergence analysis. Appl Soft Comput 31:274–292

    Article  Google Scholar 

  • Lu Z, Geng L, Huo G et al (2019) A novel hybrid multi-objective bacterial colony chemotaxis algorithm. Soft Comput 1–20

  • Müller SD, Marchetto J, Airaghi S et al (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6(1):16–29

    Article  Google Scholar 

  • Murty VVSN, Kumar A (2020) Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Prot Control Mod Power Syst 5(2)

  • Poloni C (1997) Hybrid GA for multi-objective aerodynamic shape optimization. Genetic algorithms in engineering and computer science. Wiley, New York, pp 397–414

    Google Scholar 

  • Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algortihms. Lawrence Erlbraum Associates, New Jersey, pp 93–100

  • Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. Dissertation, Massachusetts Institute of Technology

  • Srinivas N, Deb K (2000) Multiobjective function optimization using nondominated sorting genetic algorithms. IEEE Trans Evol Comput 2(3):221–248

    Google Scholar 

  • Tanaka M, Watanabe H, Furukawa Y, Tanino T (1995) GA-based decision support system for multicriteria optimization. In: IEEE international conference on systems, man and cybernetics, vol 2, pp 1556–1561

  • Valenzuela-Rendón M, Uresti-Charre E (1997) A non-generational genetic algorithm for multiobjective optimization. In: Proceedings of the seventh international conference on genetic algorithms, pp 658–665

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  • Zheng J, Zou J (2017) Multi-objective evolutionary optimization. Science, Beijing

  • Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Dissertation, Swiss Federal Institute of Technology

  • Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. IEEE Trans Evol Comput 8(2):173–195

    Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report 103

Download references

Acknowledgements

The authors would like to sincerely thank the anonymous reviewers for their valuable comments that greatly helped us improve the contents of this paper. This work was supported by National Natural Science Foundation of China [Grant Numbers 61873225, 61374098] and Natural Science Foundation of Hebei Province [Grant Number F2016203507].

Author information

Authors and Affiliations

  1. Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao, China

    Zhigang Lu, Shengjing Qi, Yao Cai & Xiaoqiang Guo

  2. Department of Automotive Engineering, Clemson University, Clemson, SC, USA

    Jiangfeng Zhang

  3. College of Engineering, Northeastern University, Boston, MA, USA

    Shifan Luo

Authors
  1. Zhigang Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shengjing Qi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiangfeng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yao Cai
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaoqiang Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Shifan Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhigang Lu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Z., Qi, S., Zhang, J. et al. An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance. Soft Comput 26, 69–87 (2022). https://doi.org/10.1007/s00500-021-06467-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06467-w

Keywords