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Hybrid Elitist Ant System For Nurse Rostering Problem

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Jaradat, Ghaith M. (Author)
مؤلفين آخرين: Al-Badareen, Anas (Co-Author), Ayob, Masri (Co-Author), Al-Smadi, Mutasem (Co-Author), Al-Marashdeh, Ibrahim (Co-Author)
المجلد/العدد: مج31, ع3
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 378 - 384
DOI: 10.33948/0584-031-003-010
ISSN: 1319-1578
رقم MD: 974706
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Metaheuristics, Elitist Ant System | External Memory | Diversification | Intensification | Nurse Rostering Problem
رابط المحتوى:
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المستخلص: The diversity and quality of high-quality and diverse-solution external memory of the hybrid Elitist-Ant System is examined in this study. The Elitist-Ant System incorporates an external memory for preserving search diversity while exploiting the solution space. Using this procedure, the effectiveness and efficiency of the search may be guaranteed which could consequently improve the performance of the algorithm and it could be well generalized across diverse problems of combinatorial optimization. The generality of this algorithm through its consistency and efficiency is tested using a Nurse-Rostering Problem. The outcomes demonstrate the competitiveness of the hybrid Elitist-Ant System’s performance within numerous datasets as opposed to those by other systems. The effectiveness of the external memory usage in search diversification is evidenced in this work. Subsequently, such usage improves the performance of the hybrid Elitist-Ant System over diverse datasets and problems. ©2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

ISSN: 1319-1578

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