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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2018

19.09.2016 | Original Article

Economic load dispatch problems with valve-point loading using natural updated harmony search

verfasst von: Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Ahamad Tajudin Khader, Asaju La’aro Bolaji, Ammar Almomani

Erschienen in: Neural Computing and Applications | Ausgabe 10/2018

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Abstract

In this paper, the update process of harmony search (HS) algorithm is modified to improve its concept of diversity. The update process in HS is based on a greedy mechanism in which the new harmony solution, created in each generation, replaces the worst individual in the population, if better. This greedy process could be improved with other updates mechanisms in order to control the diversity perfectly. Three versions of HS have been proposed: (1) Natural Proportional HS ; (2) Natural Tournament HS; (3) Natural Rank HS. These three HS versions employed the natural selection principle of the “survival of the fittest”. Instead of replacing the worst individual in population, any individual can be replaced based on certain criteria. Four versions of economic loading dispatch (ELD) problems with valve point have been used to measure the effect of the newly proposed HS versions. The results show that the new HS versions are very promising for ELD domain. This claim is proved based on the comparative evaluation process where the new HS versions are able to excel the state-of-the-art methods in almost ELD problems used.
Vorheriger Artikel A restart local search algorithm for solving maximum set k-covering problem
Nächster Artikel Reachable sets bounding for generalized neural networks with interval time-varying delay and bounded disturbances

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Metadaten
Titel
Economic load dispatch problems with valve-point loading using natural updated harmony search
verfasst von
Mohammed Azmi Al-Betar
Mohammed A. Awadallah
Ahamad Tajudin Khader
Asaju La’aro Bolaji
Ammar Almomani
Publikationsdatum
19.09.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2611-2

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