Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation
المؤلفون المشاركون
Tian, Yun
Sun, Lin
Xu, Jiucheng
Chen, Suisui
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-20، 20ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-02-10
دولة النشر
مصر
عدد الصفحات
20
التخصصات الرئيسية
الملخص EN
Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms.
The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems.
However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems.
To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP).
Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population.
By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum.
Secondly, a new RLP is defined and introduced to improve the migration operator.
This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution.
A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency.
Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets.
Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sun, Lin& Chen, Suisui& Xu, Jiucheng& Tian, Yun. 2019. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity،Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sun, Lin…[et al.]. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity No. 2019 (2019), pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sun, Lin& Chen, Suisui& Xu, Jiucheng& Tian, Yun. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity. 2019. Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131734
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