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 |
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Self-Adaptive Mussels Wandering Optimization Algorithm with Application for Artificial Neural Network Training.pdf | 640.73 kB | Adobe PDF | View/Open |
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