Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8379
Title: Self-Adaptive mussels wandering optimization algorithm with application for artificial neural network training
Authors: Abusnaina, Ahmed A. 
Abdullah, Rosni 
Kattan, Ali 
Keywords: Mussels wandering optimization;Mathematical optimization;Self-organizing systems;Self-adaptive;Metaheuristics;Neural networks (Computer science);Pattern recognition systems;Pattern classification
Issue Date: 2020
Publisher: Journal of Intelligent Systems
Abstract: The mussels wandering optimization (MWO) is a recent population-based metaheuristic optimization algorithm inspired ecologically by mussels’ movement behavior. The MWO has been used successfully for solving several optimization problems. This paper proposes an enhanced version of MWO, known as the enhanced-mussels wandering optimization (E-MWO) algorithm. The E-MWO aims to overcome the MWO shortcomings, such as lack in explorative ability and the possibility to fall in premature convergence. In addition, the E-MWO incorporates the self-adaptive feature for setting the value of a sensitive algorithm parameter. Then, it is adapted for supervised training of artificial neural networks, whereas pattern classification of real-world problems is considered. The obtained results indicate that the proposed method is a competitive alternative in terms of classification accuracy and achieve superior results in training time.
URI: http://hdl.handle.net/20.500.11889/8379
DOI: 10.1515/jisys-2017-0292
Appears in Collections:Fulltext Publications

Show full item record

Page view(s)

6
checked on Jun 18, 2024

Download(s)

2
checked on Jun 18, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.