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Neural Computing and Applications

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19.08.2016 | Original Article

Quasi-reflected ions motion optimization algorithm for short-term hydrothermal scheduling

verfasst von: Sujoy Das, Aniruddha Bhattacharya, Ajoy Kumar Chakraborty

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

This paper describes quasi-reflected ions motion optimization algorithm to solve the short-term hydrothermal scheduling problem. The aim of hydrothermal scheduling is to minimize the total cost of generation by optimizing power generation of several hydro and thermal units on an hourly basis. The algorithm mainly works on the principle that opposite charges attract each other and same charges repel each other. Two phases are employed in this algorithm, namely liquid phase and crystal phase, in order to perform exploration and exploitation. Furthermore, quasi-reflected-based learning scheme is incorporated to ions motion optimization algorithm, in order to increase the convergence speed as well as the quality of the solution. To investigate the performance of the ions motion optimization algorithm, the algorithm has been tested on seven test systems. The results obtained by the ions motion optimization algorithm have been compared with those obtained by many recently developed optimization techniques such as evolutionary programming, genetic algorithm, particle swarm optimization, differential evolution, artificial immune system, teaching–learning-based optimization, real-coded chemical-reaction-based optimization, cuckoo search algorithm and modified cuckoo search algorithm. Moreover, some statistical tests have been performed to evaluate the performance of ions motion optimization algorithm.

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Titel: Quasi-reflected ions motion optimization algorithm for short-term hydrothermal scheduling
verfasst von: Sujoy Das
Aniruddha Bhattacharya
Ajoy Kumar Chakraborty
Publikationsdatum: 19.08.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2529-8

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