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Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information
المؤلفون المشاركون
Yan, Fu
Xu, Jianzhong
Yun, Kumchol
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-36، 36ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-05
دولة النشر
مصر
عدد الصفحات
36
التخصصات الرئيسية
الملخص EN
The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves.
The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer.
However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima.
This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders).
This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO).
In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm.
The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm.
The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks.
The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yan, Fu& Xu, Jianzhong& Yun, Kumchol. 2019. Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information. Complexity،Vol. 2019, no. 2019, pp.1-36.
https://search.emarefa.net/detail/BIM-1132632
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yan, Fu…[et al.]. Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information. Complexity No. 2019 (2019), pp.1-36.
https://search.emarefa.net/detail/BIM-1132632
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yan, Fu& Xu, Jianzhong& Yun, Kumchol. Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information. Complexity. 2019. Vol. 2019, no. 2019, pp.1-36.
https://search.emarefa.net/detail/BIM-1132632
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132632
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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