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Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning
المؤلفون المشاركون
Shi, Dongyuan
Wen, Siao
Chen, Jinfu
Li, Yinhong
Duan, Xianzhong
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-23، 23ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-09-13
دولة النشر
مصر
عدد الصفحات
23
التخصصات الرئيسية
الملخص EN
Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator.
One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence.
The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated.
To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO.
Additionally, diversity mechanism is proposed.
To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed.
It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained.
The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics.
The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms.
The effects of each improved component are also analyzed.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wen, Siao& Chen, Jinfu& Li, Yinhong& Shi, Dongyuan& Duan, Xianzhong. 2017. Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-23.
https://search.emarefa.net/detail/BIM-1189804
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wen, Siao…[et al.]. Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning. Mathematical Problems in Engineering No. 2017 (2017), pp.1-23.
https://search.emarefa.net/detail/BIM-1189804
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wen, Siao& Chen, Jinfu& Li, Yinhong& Shi, Dongyuan& Duan, Xianzhong. Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-23.
https://search.emarefa.net/detail/BIM-1189804
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1189804
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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