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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2017

20-01-2016 | Original Article

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

Authors: Vikram Kumar Kamboj, Ashutosh Bhadoria, S. K. Bath

Published in: Neural Computing and Applications | Issue 8/2017

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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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Appendix
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Metadata
Title
Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer
Authors
Vikram Kumar Kamboj
Ashutosh Bhadoria
S. K. Bath
Publication date
20-01-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2148-9

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