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Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy
المؤلفون المشاركون
Wang, Lide
Chen, Huang
Di, Jun
Ping, Shen
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-27
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications.
However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum.
To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper.
In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy.
The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms.
Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm.
Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Huang& Wang, Lide& Di, Jun& Ping, Shen. 2020. Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138723
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Huang…[et al.]. Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1138723
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Huang& Wang, Lide& Di, Jun& Ping, Shen. Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138723
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138723
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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