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Neural Computing and Applications

, Volume 31, Supplement 1, pp 447–475 | Cite as

Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

  • Muhammad Asif Zahoor Raja
  • Usman Ahmed
  • Aneela Zameer
  • Adiqa Kausar Kiani
  • Naveed Ishtiaq ChaudharyEmail author
Original Article

Abstract

In the present study, bio-inspired computational heuristics are exploited for finding the solution of economic load dispatch (ELD) problem with valve point loading effect using variants of genetic algorithm (GA) hybrid with sequential quadratic programming (SQP) and interior-point algorithms (IPAs). Variants of GAs are constructed using different sets of routines for its fundamental operators in order to explore the entire search space for global optimum solutions while SQP and IPA are integrated with GAs for rapid local convergence. Nine variants of each design scheme based on GAs, GA-SQP and GA-IPAs are applied on three different ELD problems of thermal power plant systems. Comparative studies of the proposed schemes are performed through the results of statistical performance indices in order to establish the worth and effectiveness in terms of accuracy, convergence and complexity measures.

Keywords

Economic load dispatch Hybrid computing Evolutionary computations Genetic algorithms Sequential quadratic programming Interior-point algorithms 

Notes

Compliance with ethical standards

All the authors of the manuscript declare that there is no

• Potential conflicts of interest.

• Research involving human participants and/or animal.

• Material that required informed consent.

References

  1. 1.
    Kirschen D (2010). Fundamentals of Power System Economics. WileyGoogle Scholar
  2. 2.
    ZHU J (2009) Optimization of power system operation: theory and practice. 4th ed. Chichester: Wiley. IEEEGoogle Scholar
  3. 3.
    Kumar S, Chaturvedi DK (2013) Optimal power flow solution using fuzzy evolutionary and swarm optimization. Journal of Electrical Power and Energy Systems 47:416–423Google Scholar
  4. 4.
    Chatterjee K, Shankar R, Chatterjee TK (2015) Load frequency control considering very short-term load prediction and economic load dispatch using neural network and its application. Systems Thinking Approach for Social Problems, Springer India:75–89Google Scholar
  5. 5.
    Momoh JA, Elhawary ME, Adapa R (1999) A review of selected optimal power flow literature to 1993 part1: nonlinear and quadratic programming approaches. IEEE Trans Power System 4(1):96–104Google Scholar
  6. 6.
    Xia X, Elaiw AM (2010) Optimal dynamic economic dispatch of generation: a review. Electr Power Syst Res 80(8):975–986Google Scholar
  7. 7.
    Elattar EE (2015) A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Int J Electr Power Energy Syst 69:18–26Google Scholar
  8. 8.
    Ramzanpour M, Abdi H (2014) Economic load dispatch with considering the valve-point effects and ramp rate limits of generators using evolutionary algorithms. Journal of Advances in Computer Research 5(3):69–84Google Scholar
  9. 9.
    Augusteen WA, Rengaraj R, Muthu Selvan NB (2015) Phenotypic evolutionary programming for economic operation of thermal—wind coordination. Power Electronics and Renewable Energy Systems. Springer India. 1425–1436Google Scholar
  10. 10.
    Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. Evolutionary Computation, IEEE Transactions on 7.1:83–94Google Scholar
  11. 11.
    Morshed MJ, Asgharpour A (2014) Hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) algorithm for solving economic load dispatch with incorporating stochastic wind power: a comparative study on heuristic optimization techniques. Energy Convers Manag 84:30–40Google Scholar
  12. 12.
    Xiong G, Shi D, Duan X (2013) Multi-strategy ensemble biogeography-based optimization for economic dispatch problems. Appl Energy 111:801–811Google Scholar
  13. 13.
    Kavousi-Fard A et al (2014) A novel sufficient bio-inspired optimisation method based on modified krill herd algorithm to solve the economic load dispatch. International Journal of Bio-Inspired Computation 6(6):416–423MathSciNetGoogle Scholar
  14. 14.
    Jeddi B, Vahidinasab V (2014) A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Convers Manag 78:661–675Google Scholar
  15. 15.
    Mouassa S, Bouktir T (2015) Artificial Bee Colony Algorithm for Solving OPF Problem Considering the Valve Point Effect. Int J Comput Appl 112.1Google Scholar
  16. 16.
    Herbadji O, Bouktir T (2015) Optimal power flow using firefly algorithm with consideration of FACTS devices UPFC. International Journal on Electrical Engineering and Informatics 7(1):12Google Scholar
  17. 17.
    Mahdad B, Srairi K, Bouktir T (2013) Interactive DE for solving combined security environmental economic dispatch considering FACTS technology. Frontiers in Energy 7(4):429–447Google Scholar
  18. 18.
    Tran CD et al (2015) Economic load dispatch with multiple fuel options and valve point effect using cuckoo search algorithm with different distributions. International Journal of Hybrid Information Technology 8(1):305–316Google Scholar
  19. 19.
    Hosseinnezhad V et al (2014) Species-based quantum particle swarm optimization for economic load dispatch. Int J Electr Power Energy Syst 63:311–322Google Scholar
  20. 20.
    Basu M (2014) Teaching–learning-based optimization algorithm for multi-area economic dispatch. Energy 68:21–28Google Scholar
  21. 21.
    Modiri-Delshad M, Rahim NA (2014) Solving non-convex economic dispatch problem via backtracking search algorithm. Energy 77:372–381Google Scholar
  22. 22.
    Hooshmand R-A, Parastegari M, Morshed MJ (2012) Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder–mead algorithm. Appl Energy 89(1):443–453Google Scholar
  23. 23.
    Jadoun VK et al (2015) Modulated particle swarm optimization for economic emission dispatch. Int J Electr Power Energy Syst 73:80–88Google Scholar
  24. 24.
    Vlachogiannis JG, Lee KY (2009) Economic load dispatch—a comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. Power Systems, IEEE Transactions on 24.2:991–1001Google Scholar
  25. 25.
    Kim J, Kim CS, Geem ZW (2014) A memetic approach for improving minimum cost of economic load dispatch problems. Math Prob EngGoogle Scholar
  26. 26.
    Jiang S, Ji Z, Shen Y (2014) A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Int J Electr Power Energy Syst 55:628–644Google Scholar
  27. 27.
    Niu Q, Li K, Irwin GW (2015) Differential evolution combined with clonal selection for dynamic economic dispatch. Journal of Experimental & Theoretical Artificial Intelligence 27(3):325–350Google Scholar
  28. 28.
    Sivasubramani S, Ahmad M (2014) Hybrid harmony search algorithm and interior point method for economic dispatch with valve-point effect. International Journal of Emerging Electric Power Systems 15.3:253–261Google Scholar
  29. 29.
    Duman S, Yorukeren N, Altas IH (2015) A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. Int J Electr Power Energy Syst 64:121–135Google Scholar
  30. 30.
    Niknam T (2010) A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Appl Energy 87(1):327–339Google Scholar
  31. 31.
    Alsumait JS, Sykulski JK, Al-Othman AK (2010) A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems. Appl Energy 87(5):1773–1781Google Scholar
  32. 32.
    Chiroma H, Abdulkareem S, Herawan T (2015) Evolutionary neural network model for West Texas intermediate crude oil price prediction. Appl Energy 142:266–273Google Scholar
  33. 33.
    Abu Arqub, O., Abo-Hammour, Z., Momani, S. and Shawagfeh, N. (2012). Solving singular two-point boundary value problems using continuous genetic algorithm. In Abstract and Applied Analysis (Vol. 2012). Hindawi Publishing CorporationGoogle Scholar
  34. 34.
    Arqub OA (2016) The reproducing kernel algorithm for handling differential algebraic systems of ordinary differential equations. Mathematical Methods in the Applied Sciences 39(15):4549–4562MathSciNetzbMATHGoogle Scholar
  35. 35.
    Arqub OA (2016) Approximate solutions of DASs with nonclassical boundary conditions using novel reproducing kernel algorithm. Fundamenta Informaticae 146(3):231–254MathSciNetzbMATHGoogle Scholar
  36. 36.
    Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetzbMATHGoogle Scholar
  37. 37.
    Draa A, Bouaziz A (2014) An artificial bee colony algorithm for image contrast enhancement. Swarm and Evolutionary computation 16:69–84Google Scholar
  38. 38.
    Raja MAZ, Shah FH, Tariq M and Ahmad I (2016). Design of artificial neural network models optimized with sequential quadratic programming to study the dynamics of nonlinear Troesch’s problem arising in plasma physics. Neur Comput Appl 1–27. doi:  10.1007/s00521-016-2530-2
  39. 39.
    Ahmad, I., Ahmad, F., Raja, M.A.Z., Ilyas, H., Anwar, N. and Azad, Z. Intelligent computing to solve fifth-order boundary value problem arising in induction motor models. Neur Comput Appl 1–18. doi:  10.1007/s00521–016–2547-6
  40. 40.
    Dahi ZAEM, Mezioud C, Draa A (2016) A quantum-inspired genetic algorithm for solving the antenna positioning problem. Swarm and Evolutionary Computation 31:24–63Google Scholar
  41. 41.
    Raja MAZ, Shah FH, Alaidarous ES, Syam MI (2017) Design of bio-inspired heuristic technique integrated with interior-point algorithm to analyze the dynamics of heartbeat model. Appl Soft Comput 52:605–629Google Scholar
  42. 42.
    Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm and Evolutionary Computation 2:1–14Google Scholar
  43. 43.
    Ong YS, Lim MH, Chen X (2010) Memetic computation—past, present & future [research frontier]. IEEE Comput Intell Mag 5(2):24–31Google Scholar
  44. 44.
    Kim J, Kim CS, Geem ZW (2014) A memetic approach for improving minimum cost of economic load dispatch problems. Math Probl Eng 2014Google Scholar
  45. 45.
    Zaman MF, Elsayed SM, Ray T, Sarker RA (2016) Evolutionary algorithms for dynamic economic dispatch problems. IEEE Trans Power Syst 31(2):1486–1495Google Scholar
  46. 46.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan press, Ann arbor, MIGoogle Scholar
  47. 47.
    Zhu Z, Zhang F, Li C, Wu T, Han K, Lv J, Li Y, Xiao X (2015) Genetic algorithm optimization applied to the fuel supply parameters of diesel engines working at plateau. Appl Energy 157:789–797Google Scholar
  48. 48.
    Hong YY, Lai YM, Chang YR, Lee YD, Liu PW (2015) Optimizing capacities of distributed generation and energy storage in a small autonomous power system considering uncertainty in renewables. Energies 8(4):2473–2492Google Scholar
  49. 49.
    Shahrokhabadi S, Khoshfahm V, Rafsanjani HN (2014) Hybrid of natural element method (NEM) with genetic algorithm (GA) to find critical slip surface. Alexandria Engineering Journal 53(2):373–383Google Scholar
  50. 50.
    Shahrokhabadi S, Toufigh MM (2013) The solution of unconfined seepage problem using natural element method (NEM) coupled with genetic algorithm (GA). Appl Math Model 37(5):2775–2786MathSciNetzbMATHGoogle Scholar
  51. 51.
    Ruiz GR, Bandera CF, Temes TGA, Gutierrez ASO (2016) Genetic algorithm for building envelope calibration. Appl Energy 168:691–705Google Scholar
  52. 52.
    Nocedal J, Wright SJ (2006) Sequential quadratic programming. Num Opt 529–562Google Scholar
  53. 53.
    Subathra MSP, Selvan S, Victoire T, Christinal AH, Amato U (2015) A hybrid with cross-entropy method and sequential quadratic programming to solve economic load dispatch problem. Systems Journal, IEEE 9(3):1031–1044Google Scholar
  54. 54.
    Han X, Quan L, Xiong X (2015) A modified gravitational search algorithm based on sequential quadratic programming and chaotic map for ELD optimization. Knowl Inf Syst 42(3):689–708Google Scholar
  55. 55.
    Nesterov Y, Nemirovskii A, Ye Y (1994) Interior-point polynomial algorithms in convex programming (Vol. 13). Society for industrial and applied mathematics, PhiladelphiazbMATHGoogle Scholar
  56. 56.
    Duan C, Fang W, Jiang L, Liu J (2015) Adaptive barrier filter-line-search interior point method for optimal power flow with FACTS devices.Generation. Transmission & Distribution, IET 9(16):2792–2798Google Scholar
  57. 57.
    Sindekar AS, Agrawal AR, Pande VN (2013) Comparison of some optimization techniques for efficiency optimization of induction motor. International Journal of Engineering Science and Technology 5(6):1303Google Scholar
  58. 58.
    Sivasubramani S, Ahmad M (2014) Hybrid harmony search algorithm and interior point method for economic dispatch with valve-point effect. International Journal of Emerging Electric Power Systems 15(3):253–261Google Scholar
  59. 59.
    Victoire TAA, Jeyaku Mar AE (2004) Hybrid PSO–SQP for economic dispatch with valve point effect. Int J Elect Power Energy Syst 71(1):51–59Google Scholar
  60. 60.
    Hemamalini S, Simon SP (2008) Emission constrained economic dispatch with valve-point effect using particle swarm optimization. IEEE Region 10 Conference TenconGoogle Scholar
  61. 61.
    Kim JO, Shin D-J, Park J-N, Singh C (2002) Atavistic genetic algorithm for economic dispatch with valve point effect. Elsevier Science BV Electric Power Systems Research 62:201–207Google Scholar
  62. 62.
    Kurt A, Çenesiz Y, Tasbozan O (2015) On the solution of burgers’ equation with the new fractional derivative. Open Phys 13:355–360Google Scholar
  63. 63.
    Çenesiz Y, Baleanu D, Kurt A, Tasbozan O (2016) New exact solutions of Burgers’ type equations with conformable derivative. Waves Random Complex Media 1–14Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Muhammad Asif Zahoor Raja
    • 1
  • Usman Ahmed
    • 1
  • Aneela Zameer
    • 2
  • Adiqa Kausar Kiani
    • 3
  • Naveed Ishtiaq Chaudhary
    • 4
    Email author
  1. 1.Department of Electrical EngineeringCOMSATS Institute of Information TechnologyAttockPakistan
  2. 2.Department of Computer and Information SciencePakistan Institute of Engineering and Applied SciencesNilorePakistan
  3. 3.Department of EconomicsFederal Urdu University of Arts Science and TechnologyIslamabadPakistan
  4. 4.Department of Electrical EngineeringInternational Islamic UniversityIslamabadPakistan

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