Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization
المؤلفون المشاركون
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-07-24
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The cooperative coevolution (CC) algorithm features a “divide-and-conquer” problem-solving process.
This feature has great potential for large-scale global optimization (LSGO) while inducing some inherent problems of CC if a problem is improperly decomposed.
In this work, a novel CC named selective multiple population- (SMP-) based CC (CC-SMP) is proposed to enhance the cooperation of subproblems by addressing two challenges: finding informative collaborators whose fitness and diversity are qualified and adapting to the dynamic landscape.
In particular, a CMA-ES-based multipopulation procedure is employed to identify local optima which are then shared as potential informative collaborators.
A restart-after-stagnation procedure is incorporated to help the child populations adapt to the dynamic landscape.
A biobjective selection is also incorporated to select qualified child populations according to the criteria of informative individuals (fitness and diversity).
Only selected child populations are active in the next evolutionary cycle while the others are frozen to save computing resource.
In the experimental study, the proposed CC-SMP is compared to 7 state-of-the-art CC algorithms on 20 benchmark functions with 1000 dimensionality.
Statistical comparison results figure out significant superiority of the CC-SMP.
In addition, behavior of the SMP scheme and sensitivity to the cooperation frequency are also analyzed.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Peng, Xingguang& Wu, Yapei. 2018. Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization. Complexity،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136653
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Peng, Xingguang& Wu, Yapei. Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization. Complexity No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1136653
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Peng, Xingguang& Wu, Yapei. Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization. Complexity. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136653
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1136653
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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