کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6903130 1446750 2018 37 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dynamic multi-swarm differential learning particle swarm optimizer
ترجمه فارسی عنوان
بهینه سازی ذرات ذره بین یادگیری دیفرانسیل چند درهم
کلمات کلیدی
هوشافزاری بهینه سازی ذرات ذرات، پویایی زیر سحابی، جهش دیفرانسیل،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
Because different optimization algorithms have different search behaviors and advantages, hybrid strategy is one of the main research directions to improve the performance of PSO. Inspired by this idea, a dynamic multi-swarm differential learning particle swarm optimizer (DMSDL-PSO) is proposed in this paper. We propose a novel method to merge the differential evolution operator into each sub-swarm of the DMSDL-PSO. Combining the exploration capability of the differential mutation and employing Quasi-Newton method as a local searcher to enhance the exploitation capability, DMSDL-PSO has a good exploration and exploitation capability. According to the characteristics of the DMSDL-PSO, three modified differential mutation operators are discussed. Differential mutation is adopted for the personal historically best particle. Because the velocity updating equation of the particles in PSO has some shortcomings, a modified velocity updating equation is adopted in DMSDL-PSO. In DMSDL-PSO, in which the particles are divided into several small and dynamic sub-swarms. The dynamic change of sub-swarms can promote the information exchange of the whole swarm. In order to test the performance of DMSDL-PSO, 41 benchmark functions are adopted. Lots of numerical experiments are conducted to compare DMSDL-PSO with other popular algorithms. The numerical results demonstrate that DMSDL-PSO performs better on some benchmark functions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Swarm and Evolutionary Computation - Volume 39, April 2018, Pages 209-221
نویسندگان
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