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Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer

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Abstract

Ant lion optimizer (ALO) is a newly developed population-based search algorithm inspired by hunting mechanism of antlions and based on five steps of hunting the ants, i.e., the random walk of ants, building traps, entrapment of ants in traps, catching preys and re-building traps. This paper presents the application of ALO algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of ALO algorithm is tested for economic load dispatch problem of four IEEE benchmarks of small-scale power systems, and the results are verified by a comparative study with lambda iteration method, particle swarm optimization algorithm, genetic algorithm, artificial bee colony, evolutionary programming and Grey Wolf optimizer (GWO). Comparative results show that the performance of ant lion optimizer algorithm is better than recently developed GWO algorithm and other well-known heuristics and meta-heuristics search algorithms.

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Acknowledgments

The authors wish to thank Dr. Seyedali Mirjalili, Professor, School of Information and Communication Technology, Griffith University, Nathan, Brisbane, Australia, for their continuous support for implementing the ALO algorithm for economic load dispatch problem and Dr. J.S. Dhillon, Professor, Sant Longowal Institute of Engineering and Technology, Punjab (India), for their guidance, continuous support and encouragement and DAV University, Jalandhar, for providing advanced research facilities during research work.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, DAV University, Jalandhar, Punjab, India

    Vikram Kumar Kamboj

  2. Research Scholar, Department of Instrumentation and Control Engineering, National Institute of Technology, Jalandhar, Punjab, India

    Ashutosh Bhadoria

  3. Department of Electrical Engineering, Giani Zail Singh Campus College of Engineering and Technology, Bathinda, Punjab, India

    S. K. Bath

Authors
  1. Vikram Kumar Kamboj
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  2. Ashutosh Bhadoria
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  3. S. K. Bath
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Corresponding author

Correspondence to Vikram Kumar Kamboj.

Appendix

Appendix

 % MATLAB codes for the function B proposed by Seyedali Mirjalili [59]

function [M Ant, M Antlion] = B(M Ant, M Antlion)

for i = 1: n

  Antlion = RouletteWheelSelection (M Antlion, 1/MOAL)

Update c and d using Eqs. (13) and (14)

  c = Antlion + c

  d = Antlion + d

  celite = Elite + c

  delite = Elite + d

for j = 1: d

  R A  = normalize(R A , c, d)

  R E  = X(t)

  R E  = normalize(RE, Celite,delite)

  M Ant (i,j) = (R A  + R E )/2

end

end

M OA = FitnessFunction(MAnt)

M combined = concanenation of M Ant and M Antlion

M combined = sort(Mcombined)

M Antlion = the first n rows of Mcombined

If fintessfunction(M Antlion(1,:)) < f(Elite)

Elite = M Antlion(1, :)

end

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Kamboj, V.K., Bhadoria, A. & Bath, S.K. Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Comput & Applic 28, 2181–2192 (2017). https://doi.org/10.1007/s00521-015-2148-9

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  • DOI: https://doi.org/10.1007/s00521-015-2148-9

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