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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.<\/jats:p>","DOI":"10.1515\/jisys-2017-0292","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T05:48:18Z","timestamp":1519710498000},"page":"345-363","source":"Crossref","is-referenced-by-count":5,"title":["Self-Adaptive Mussels Wandering Optimization Algorithm with Application for Artificial Neural Network Training"],"prefix":"10.1515","volume":"29","author":[{"given":"Ahmed A.","family":"Abusnaina","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering and Technology , Birzeit University , Birzeit, Ramallah , Palestine"}]},{"given":"Rosni","family":"Abdullah","sequence":"additional","affiliation":[{"name":"School of Computer Sciences , Universiti Sains Malaysia , 11800, Penang , Malaysia"}]},{"given":"Ali","family":"Kattan","sequence":"additional","affiliation":[{"name":"IT Department , Ishik University , Qazi Muhammad, Erbil , Iraq"}]}],"member":"374","published-online":{"date-parts":[[2018,2,21]]},"reference":[{"key":"2025120523293271411_j_jisys-2017-0292_ref_001","unstructured":"A. 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