A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning
المؤلفون المشاركون
Xu, Lizhang
Lu, En
Li, Yaoming
Ma, Zheng
Tang, Zhong
Luo, Chengming
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-11-22
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO).
Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number.
The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed.
Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence.
This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm.
Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments.
The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants.
Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, En& Xu, Lizhang& Li, Yaoming& Ma, Zheng& Tang, Zhong& Luo, Chengming. 2019. A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1198069
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, En…[et al.]. A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning. Mathematical Problems in Engineering No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1198069
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, En& Xu, Lizhang& Li, Yaoming& Ma, Zheng& Tang, Zhong& Luo, Chengming. A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1198069
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1198069
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