الگوریتم بهینه سازی توده ذرات بر اساس تشخیص هدفمند
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الگوریتم بهینه سازی توده ذرات بر اساس تشخیص هدفمند

عنوان فارسی مقاله: الگوریتم بهینه سازی توده ذرات چند گروهی بر اساس تشخیص هدفمند و توپولوژی دینامیکی
عنوان انگلیسی مقاله: A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی و مهندسی نرم افزار
کلمات کلیدی فارسی: بهینه سازی توده ذرات، Dynamic sub-swarm number، دسته بندی مجدد زیر گروه، شناسایی هدفمند، جستجوی محلی
کلمات کلیدی انگلیسی: Particle swarm optimization، Dynamic sub-swarm number، Sub-swarm regrouping، Purposeful detecting، Local-searching
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2018.02.042
دانشگاه: School of Software, East China Jiaotong University, Nanchang 330013, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 6/031 در سال 2018
شاخص H_index: 110 در سال 2019
شاخص SJR: 1/216 در سال 2018
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 37
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11310
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related works


3- MSPSO


4- Experimental verification and comparisons


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of three novel strategies to balance the exploration and exploitation abilities. The new proposed MSPSO in this work is based on multiple swarms framework cooperating with the dynamic sub-swarm number strategy (DNS), sub-swarm regrouping strategy (SRS), and purposeful detecting strategy (PDS). Firstly, the DNS divides the entire population into many sub-swarms in the early stage and periodically reduces the number of sub-swarms (i.e., increase the size of each sub-swarm) along with the evolutionary process. This is helpful for balancing the exploration ability early and the exploitation ability late, respectively. Secondly, in each DNS period with special number of sub-swarms, the SRS is to regroup these sub-swarms based on the stagnancy information of the global best position. This is helpful for diffusing and sharing the search information among different sub-swarms to enhance the exploitation ability. Thirdly, the PDS is relying on some historical information of the search process to detect whether the population has been trapped into a potential local optimum, so as to help the population jump out of the current local optimum for better exploration ability. The comparisons among MSPSO and other 13 peer algorithms on the CEC2013 test suite and 4 real applications suggest that MSPSO is a very reliable and highly competitive optimization algorithm for solving different types of functions. Furthermore, the extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO.


Introduction


Particle swarm optimization algorithm (PSO) is a widely known evolutionary algorithm proposed by Kennedy and Eberhart in 1995 [1, 2]. During the optimization process, each particle adjusts its flight direction and step-size re5 lying on the information extracted from the past experience of itself and its neighbors. Although the search pattern of each particle is quite simple, the search behavior of the entire population is very complex, and the population shows great intelligence owing to the cooperative behavior among particles. Due to the simplicity of implementation, PSO has been applied for many academic 10 and real-world applications [3, 4, 5]. Extensive studies reveal that PSO’s performance mainly depends upon its two characteristics [6, 7]: exploration and exploitation. However, there is a contradiction between the two capabilities. In order to be successful, PSO needs to establish a good ratio between exploration and exploitation. A common 15 belief is that PSO should start with exploration and then gradually change into exploitation. Hence, many time-varying strategies are proposed to regulate the parameters and neighbor topology involved in PSO. For example, in the most ubiquitous update rules of parameters introduced in [8, 9], three parameters involved in PSO are adjusted based on the iteration 20 numbers aiming to meet different search requirements of different evolutionary stages. The fundament thought of these modifications is tuning particles’ learning weights for their exemplars.

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