Skip to main content
SpringerLink
Log in

Improved multi-objective artificial bee colony algorithm for optimal power flow problem

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

The artificial bee colony (ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow (OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. HABIABOLLAHZADEH H, LUO G, SEMLYEN A. Hydrothermal optimal power flow based on combined linear and nonlinear programming methodology [J]. IEEE Transactions on Power Systems, 1989, 4(2): 530–537.

    Article  Google Scholar 

  2. AJAMI A, ARMAGHAN M. A multi-objective gravitational search algorithm based approach of power system stability enhancement with UPFC [J]. Journal of Central South University, 2013, 6: 1536–1544.

    Google Scholar 

  3. BAKIRTZIS A G, BISKAS P N, ZOUMAS C E, PETRIDIS V. Optimal power flow by enhanced genetic algorithm [J]. IEEE Transactions on Power Systems, 2002, 17: 229–236.

    Article  Google Scholar 

  4. SOUSA T, SOARES J, VALE Z A, MORAIS H, FARIA P. Simulated annealing metaheuristic to solve the optimal power flow [C]// IEEE Power and Energy Society General Meeting. 2011: 1–8.

    Google Scholar 

  5. ABIDO M A. Optimal power flow using tabu search algorithm [J]. Electric Power Components and Systems, 2002, 30: 469–483.

    Article  Google Scholar 

  6. SAYAH S, ZEHAR K. Modified differential evolution algorithm for optimal power flow with non-smooth cost functions [J]. Energy Conversion and Management, 2008, 49: 3036–3042.

    Article  Google Scholar 

  7. SIVASUBRAMANI S, SWARUP K S. Multi-objective harmony search algorithm for optimal power flow problem [J]. Electrical Power and Energy Systems, 2011, 33: 745–752.

    Article  Google Scholar 

  8. ROY P K, GHOSHAL S P, THAKUR S S. Biogeography based optimization for multiconstraint optimal power flow with emission and non-smooth cost function [J]. Expert Systems with Applications, 2010, 37: 8221–8228.

    Article  Google Scholar 

  9. EBERCHART R C, KENNEDY J. A new optimizer using particle swarm theory [C]// Proceeding of the 6th International Symposium on Micromachine and Human Science. Nagoya, Japan, 1995: 39–43.

    Google Scholar 

  10. MA Lian-bo, HU Kun-yuan, ZHU Yun-long, CHEN Han-ning. Computational evolution of social behavior in bacterial colony optimization model [J]. Journal of Pure and Applied Microbiology, 2013, 7: 487–493.

    Google Scholar 

  11. KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm [J]. Applied Mathematics and Computation, 2009, 214: 108–132.

    Article  MATH  MathSciNet  Google Scholar 

  12. ZHOU Wen-ping, ZHU Yun-long, CHEN Han-ning. Solving multi-objective optimization problems using artificial bee colony algorithm [J]. Discrete Dynamics in Nature and Society, 2011:569784–569784.

    Google Scholar 

  13. HEDAYATZADEH R, HASANIZADEH B, AKBARI R, ZIARATI K. A multi-objective artificial bee colony for optimizing multi-objective problems [C]// 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). Chengdu, China, 2010: 277–281.

    Google Scholar 

  14. DEB K, PRATAP A, AGARWAL S, MEYARIVAN T. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Trans Evol Comput, 2002, 6(2): 182–197.

    Article  Google Scholar 

  15. ZITZLER E, LAUMANNS M, THIELE L. SPEA2: Improving the strength pareto evolutionary algorithm [C]// Proc EUROGEN 2001: Evolutionary Methods Design Optimization Control Appl Ind Problems. Athens, Greece, 2002: 95–100.

    Google Scholar 

  16. CARLOS A, PULIDO G T, MAXIMINO S L. Handling multiple objectives with particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256–279.

    Article  Google Scholar 

  17. WALTERS D C, SHEBLE G B. Genetic algorithm solution of economic dispatch with valve point loading [J]. IEEE Transactions on Power Systems, 1993, 8: 1325–1332.

    Article  Google Scholar 

  18. POTTER M A, de JONG K A. Cooperative coevolution: An architecture for evolving coadapted subcomponents [J]. Evolutionary Computation, 2000, 8: 1–29.

    Article  Google Scholar 

  19. YE Bin, ZHANG Peng-xiang, ZHAO Bo, CAO Yi-jia. Multiobjective hybrid evolutionary algorithm for economic load dispatch [J]. Proceeding of the CSU-EPSA, 2007, 19(2): 67–72. (in Chinese)

    Google Scholar 

  20. ALSAC O, STOTT B. Optimal load flow with steady-state security [J]. IEEE Trans. Power Appar Syst, 1974, 93(3): 745–751.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Lian-bo Ma  (马连博), Kun-yuan Hu  (胡琨元), Yun-long Zhu  (朱云龙) & Han-ning Chen  (陈瀚宁)

  2. University of Chinese Academy of Sciences, Beijing, 100039, China

    Lian-bo Ma  (马连博)

Authors
  1. Lian-bo Ma  (马连博)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kun-yuan Hu  (胡琨元)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-long Zhu  (朱云龙)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han-ning Chen  (陈瀚宁)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lian-bo Ma  (马连博).

Additional information

Foundation item: Projects(61105067, 61174164) supported by the National Natural Science Foundation of China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Lb., Hu, Ky., Zhu, Yl. et al. Improved multi-objective artificial bee colony algorithm for optimal power flow problem. J. Cent. South Univ. 21, 4220–4227 (2014). https://doi.org/10.1007/s11771-014-2418-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-014-2418-1

Key words

Search

Navigation