Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Environmental/Economic Dispatch Problem

  • Jane-Jing Liang
  • Wei-Xing Zhang
  • Bo-Yang Qu
  • Tie-Jun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)


This paper presents a new multiobjective particle swarm optimization (MOPSO) technique to solve environmental/economic dispatch (EED) problem. The EED problem is a non-linear constrained multiobjective optimization problem. The Multi-objective Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-MO-PSO) proposed employs novel pbest and lbest updating criteria which are more suitable for solving multi-objective problems. In this work, the standard IEEE 30-bus six-generator test system is used and simulation results showed that the proposed approach is efficient and confirms its potential to solve the multiobjective EED problem.


environmental/economic dispatch particle warm optimization multi-objective optimization evolutionary algorithm 


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  1. 1.
    IEEE Current Operating Problems Working Group, Potential Impacts of Clean Air Regulations on System Operations, pp. 647–656 (1995)Google Scholar
  2. 2.
    Zahavi, J., Eisenberg, L.: An Application of the Economic-environmental Power Dispatch. IEEE Trans. Syst., Man, Cybernet., 523–530 (1977)Google Scholar
  3. 3.
    Wang, L.F., Singh, C.: Stochastic Economic Emission Based Load Dispatch Through a Modified Particle Swarm Optimization Algorithm. Electric Power Systems Research 78, 1466–1476 (2008)CrossRefGoogle Scholar
  4. 4.
    Niu, B., Wang, H., Tan, L., Xu, J.: Multi-objective Optimization Using BFO Algorithm. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS (LNBI), vol. 6840, pp. 582–587. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Abido, M.A.: A Novel Multiobjective Evolutionary Algorithm for Environmental/Economic Power Dispatch. Electric Power Systems Research, 71–81 (2003)Google Scholar
  6. 6.
    Abido, M.A.: A Niched Pareto Genetic Algorithm for Environmental/Economic Power Dispatch. Electric Power Systems Research, 97–105 (2003)Google Scholar
  7. 7.
    Abido, M.A.: Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. IEEE Trans. Evolut. Comput. 10(3), 315 (2006)CrossRefGoogle Scholar
  8. 8.
    Basu, M.: Dynamic Economic Emission Dispatch Using Nondominated Sorting Genetic Algorithm-II. Electric Power Energy Systems 30(2), 140–210 (2008)CrossRefGoogle Scholar
  9. 9.
    Niu, B., Xue, B., Li, L.: Symbiotic Multi-swarm PSO for Portfolio Optimization. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS (LNAI), vol. 5755, pp. 776–784. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Shubham, A., Panigrahi, B.K., Tiwari, M.K.: Multiobjective Particle Swarm Algorithm with Fuzzy Clustering for Electrical Power Dispatch. IEEE Trans. Evolutionary Computation, 529–541 (2008)Google Scholar
  11. 11.
    Lu, S., Sun, C., Lu, Z.: An Improved Quantum-behaved Particle Swarm Optimization Method for Short-term Combined Economic Emission Hydrothermal Scheduling. Energy Conversion and Management, 561–571 (2010)Google Scholar
  12. 12.
    Wang, L., Singh, C.: Environmental/Economic Power Dispatch Using a Fuzzified Multi-objective Particle Swarm Optimization. Electric Power Systems Research, 1654–1664 (2007)Google Scholar
  13. 13.
    Cai, J., Ma, X., Li, Q., Li, L., Peng, H.: A Multi-objective Chaotic Particle Swarm Optimization for Environmental/economic Dispatch. Energy Conversion and Management, 1318–1325 (2009) Google Scholar
  14. 14.
    Liang, J.J., Qu, B.Y., Suganthan, P.N.: Dynamic Multi-Swarm Particle Swarm Optimization for Multi-Objective Optimization Problems. Has been accepted by IEEE Congress on Evolutionary Computation (2012)Google Scholar
  15. 15.
    Hemamalini, S., Sishaj, P.S.: Emission Constrained Economic Dispatch with Valve-Point Effect using Particle Swarm Optimization. In: TENCON 2008 – 2008 IEEE Region 10 Conference, pp. 1–6 (2008)Google Scholar
  16. 16.
    Liang, J.J., Suganthan, P.N.: Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, pp. 522–528 (2005)Google Scholar
  17. 17.
    Liang, J.J., Suganthan, P.N.: Dynamic Multi-Swarm Particle Swarm Optimizer. In: Proceedings of IEEE International Swarm Intelligence Symposium (SIS 2005), pp. 124–129 (2005)Google Scholar
  18. 18.
    Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186, 311–338 (2000)zbMATHCrossRefGoogle Scholar
  19. 19.
    Wu, Y.L., Xu, L.Q., Zhang, J.: Multiobjective Particle Swarm Optimization Based on Differential Eevolution for Environmental/Economic Dispatch problem. In: Control and Decision Conference (CCDC), Chinese, pp. 1498–1503 (2011)Google Scholar
  20. 20.
    Abido, M.A.: Environmental/Economic Power Dispatch Using Multiobjective Evolutionary Algorithms. IEEE Trans. Power Systems, 1529–1537 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jane-Jing Liang
    • 1
  • Wei-Xing Zhang
    • 1
  • Bo-Yang Qu
    • 2
  • Tie-Jun Chen
    • 1
  1. 1.School of Electrical EngineeringZhengzhou UniverisityZhengzhouChina
  2. 2.School of Electric and Information EngineeringZhongyuan University of TechnologyZhengzhouChina

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