An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies
المؤلفون المشاركون
Meng, Xuelei
Gao, Mingxia
An, Mei-qing
Li, Yin-zhen
Xiang, Wan-li
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-11-01
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces.
However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin.
In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper.
In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated.
In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively.
Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE.
In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xiang, Wan-li& Meng, Xuelei& An, Mei-qing& Li, Yin-zhen& Gao, Mingxia. 2015. An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-15.
https://search.emarefa.net/detail/BIM-1057678
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xiang, Wan-li…[et al.]. An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-15.
https://search.emarefa.net/detail/BIM-1057678
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xiang, Wan-li& Meng, Xuelei& An, Mei-qing& Li, Yin-zhen& Gao, Mingxia. An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-15.
https://search.emarefa.net/detail/BIM-1057678
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1057678
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