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Neural Computing and Applications

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01.07.2016 | Original Article

Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer

verfasst von: Vikram Kumar Kamboj, S. K. Bath, J. S. Dhillon

Erschienen in: Neural Computing and Applications | Ausgabe 5/2016

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Abstract

Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.

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Titel: Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer
verfasst von: Vikram Kumar Kamboj
S. K. Bath
J. S. Dhillon
Publikationsdatum: 01.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1934-8

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