Evolutionary Intelligence

, Volume 12, Issue 2, pp 305–319 | Cite as

Multiobjective environmental adaptation method for solving environmental/economic dispatch problem

  • Tribhuvan SinghEmail author
  • Krishn Kumar Mishra
  • Ranvijay
Research Paper


Environmental adaptation method is one of the evolutionary algorithms for solving single objective optimization problems. Although the algorithm converges very fast and produces diversified solutions, there are three weaknesses in it. In this paper, first we have given the solutions to resolve these weaknesses and then we have extended the modified method to deal with multiple conflicting objectives simultaneously. A permutation-based multiobjective environmental adaptation method (pMOEAM) has been suggested to solve the environmental/economic dispatch (EED) problem of the power system. In this paper, total generation cost and environmental emission have been taken as two objectives that need to be minimized simultaneously while meeting the load demand under equality and inequality constraints. Three test systems are considered to evaluate the performance of the proposed algorithm. The performance of the suggested algorithm is compared against five multiobjective algorithms. Extensive experimental results demonstrated that the pMOEAM method can obtain effective and feasible solutions for EED problem.


Environmental/economic dispatch problem Environmental adaptation method Multiobjective evolutionary algorithms Multiobjective optimization problems 



  1. 1.
    Abido M (2003) Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Trans Power Syst 18(4):1529–1537CrossRefGoogle Scholar
  2. 2.
    Abido M (2003) A niched pareto genetic algorithm for multiobjective environmental/economic dispatch. Int J Electr Power Energy Syst 25(2):97–105CrossRefGoogle Scholar
  3. 3.
    Abido M (2009) Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electr Power Syst Res 79(7):1105–1113CrossRefGoogle Scholar
  4. 4.
    Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evolut Comput 10(3):315–329CrossRefGoogle Scholar
  5. 5.
    Baldwin JM (1896) A new factor in evolution (continued). Am Nat 30(355):536–553CrossRefGoogle Scholar
  6. 6.
    Bhattacharya A, Chattopadhyay PK (2011) Solving economic emission load dispatch problems using hybrid differential evolution. Appl Soft Comput 11(2):2526–2537CrossRefGoogle Scholar
  7. 7.
    Cai J, Ma X, Li L, Haipeng P (2007) Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Convers Manag 48(2):645–653CrossRefGoogle Scholar
  8. 8.
    Cai J, Ma X, Li Q, Li L, Peng H (2009) A multi-objective chaotic particle swarm optimization for environmental/economic dispatch. Energy Convers Manag 50(5):1318–1325CrossRefGoogle Scholar
  9. 9.
    Cai J, Ma X, Li Q, Li L, Peng H (2010) A multi-objective chaotic ant swarm optimization for environmental/economic dispatch. Int J Electr Power Energy Syst 32(5):337–344CrossRefGoogle Scholar
  10. 10.
    Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8(3):256–279CrossRefGoogle Scholar
  11. 11.
    Cortes OAC, Rau-Chaplin A (2016) Enhanced multiobjective population-based incremental learning with applications in risk treaty optimization. Evolut Intell 9(4):153–165CrossRefGoogle Scholar
  12. 12.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  13. 13.
    Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. In: Abraham A, Goldberg R (eds) Evolutionary multiobjective optimization. Springer, Berlin, pp 105–145CrossRefGoogle Scholar
  14. 14.
    Dhillon J, Parti S, Kothari D (1993) Stochastic economic emission load dispatch. Electr Power Syst Res 26(3):179–186CrossRefGoogle Scholar
  15. 15.
    dos Santos Coelho L, Mariani VC (2009) An improved harmony search algorithm for power economic load dispatch. Energy Convers Manag 50(10):2522–2526CrossRefGoogle Scholar
  16. 16.
    Farag A, Al-Baiyat S, Cheng T (1995) Economic load dispatch multiobjective optimization procedures using linear programming techniques. IEEE Trans Power Syst 10(2):731–738CrossRefGoogle Scholar
  17. 17.
    Júnior JDAB, Nunes MVA, Nascimento MHR, Rodríguez JLM, Leite JC (2017) Solution to economic emission load dispatch by simulated annealing: case study. Electr Eng 100:1–13Google Scholar
  18. 18.
    King TD, El-Hawary M, El-Hawary F (1995) Optimal environmental dispatching of electric power systems via an improved hopfield neural network model. IEEE Trans Power Syst 10(3):1559–1565CrossRefGoogle Scholar
  19. 19.
    Kumar S, Sharma B, Sharma VK, Poonia RC (2018) Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol Intell. Google Scholar
  20. 20.
    Li K, Zhang Q, Kwong S, Li M, Wang R (2014) Stable matching-based selection in evolutionary multiobjective optimization. IEEE Trans Evolut Comput 18(6):909–923CrossRefGoogle Scholar
  21. 21.
    Li Y, Wang J, Zhao D, Li G, Chen C (2018) A two-stage approach for combined heat and power economic emission dispatch: combining multi-objective optimization with integrated decision making. Energy 162:237–254CrossRefGoogle Scholar
  22. 22.
    Li Y, Yang Z, Zhao D, Lei H, Cui B, Li S (2019) Incorporating energy storage and user experience in isolated microgrid dispatch using a multi-objective model. arXiv preprint arXiv:1901.08466
  23. 23.
    Lim D, Ong Y-S, Gupta A, Goh CK, Dutta PS (2016) Towards a new praxis in optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization. Evolut Intell 9(4):203–220CrossRefGoogle Scholar
  24. 24.
    Manteaw ED, Odero NA (2012) Multi-objective environmental/economic dispatch solution using abc\_pso hybrid algorithm. Int J Sci Res Publ 2:12Google Scholar
  25. 25.
    Miettinen K (1999) Nonlinear multiobjective optimization. Volume 12 of international series in operations research and management science. Springer, BerlinGoogle Scholar
  26. 26.
    Moraes NM, Bezerra UH, Moya Rodríguez JL, Nascimento MHR, Leite J C (2018) A new approach to economic-emission load dispatch using NSGA II. Case study. Int Trans Electr Energy Syst 28(11):e2626CrossRefGoogle Scholar
  27. 27.
    Palanichamy C, Babu NS (2008) Analytical solution for combined economic and emissions dispatch. Electr Power Syst Res 78(7):1129–1137CrossRefGoogle Scholar
  28. 28.
    Rahmani R, Othman MF, Yusof R, Khalid M (2012) Solving economic dispatch problem using particle swarm optimization by an evolutionary technique for initializing particles. J Theor Appl Inf Technol 46(2):526–536Google Scholar
  29. 29.
    Saxena N, Mishra K (2017) Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking. Appl Intell 47:1–20CrossRefGoogle Scholar
  30. 30.
    Sharma B, Prakash R, Tiwari S, Mishra K (2017) A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem. Appl Intell 47:1–21CrossRefGoogle Scholar
  31. 31.
    Talaq J, El-Hawary F, El-Hawary M (1994) A summary of environmental/economic dispatch algorithms. IEEE Trans Power Syst 9(3):1508–1516CrossRefGoogle Scholar
  32. 32.
    Teng S-H et al (2016) Scalable algorithms for data and network analysis. Found Trends® Theor Comput Sci 12(1–2):1–274MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731CrossRefGoogle Scholar
  34. 34.
    Zhang R, Zhou J, Mo L, Ouyang S, Liao X (2013) Economic environmental dispatch using an enhanced multi-objective cultural algorithm. Electr Power Syst Res 99:18–29CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentMotilal Nehru National Institute of Technology AllahabadAllahabadIndia

Personalised recommendations