An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms
المؤلفون المشاركون
Sun, Qian
Yang, Jungang
Xu, Qingzheng
Fei, Rong
Wang, Na
Wang, Lei
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-08
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and several algorithms have been proposed in the literature.
Unfortunately, knowledge transfer between constituent tasks may cause negative effect on algorithm performance, especially when the optimal solutions of all tasks are in different locations of the unified search space.
To address this issue, an effective variable transformation strategy and the corresponding inverse transformation are proposed in multitasking optimization scenario.
After using variable transformation strategy, the estimated optimal solutions of all tasks are both near the center point of the unified search space.
More importantly, this strategy can enhance the task similarity, and then the effectiveness of knowledge transfer will probably be positive in this case, which can help us to improve the algorithm performance.
Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution.
In MTDE-VT, the individuals in the original population are first transformed into new locations by the variable transformation strategy.
Once the offspring is generated in the transformed unified search space, it must be transformed back to the original unified search space.
The statistical analysis of experimental results on some multitasking optimization benchmark problems illustrates the superiority of the proposed MTDE-VT algorithm in terms of solution accuracy and robustness.
Furthermore, the basic principle and the good parameter combination are also provided based on massive simulated data.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xu, Qingzheng& Wang, Lei& Yang, Jungang& Wang, Na& Fei, Rong& Sun, Qian. 2020. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xu, Qingzheng…[et al.]. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xu, Qingzheng& Wang, Lei& Yang, Jungang& Wang, Na& Fei, Rong& Sun, Qian. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144615
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