کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6857421 | 665202 | 2016 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accelerating particle swarm optimization using crisscross search
ترجمه فارسی عنوان
بهینه سازی ذرات شتاب دهنده با استفاده از جستجوی کراس اورست
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Although the particle swarm optimization (PSO) algorithm has been widely used to solve many real world problems, it is likely to suffer entrapment in local optima when addressing multimodal optimization problems. In this paper, we report a novel hybrid optimization algorithm called crisscross search particle swarm optimization (CSPSO), which is different from PSO and its variants in that each particle is directly represented by pbest. The population of particles in CSPSO is updated by modified PSO as well as crisscross search optimization (CSO) in sequence within each iteration. CSO is incorporated as an evolutionary catalytic agent that has powerful capability of searching for pbests of high quality, therefore accelerating the global convergence of PSO. CSO enhances PSO by two search operators, namely horizontal crossover and vertical crossover. The horizontal crossover further enhances PSO's global convergence ability while the vertical crossover can enhance swarm diversity. Several benchmark functions are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that CSPSO outperforms other state-of-the-art PSO variants significantly in terms of global search ability and convergence speed on almost all of the benchmark functions tested.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 329, 1 February 2016, Pages 52-72
Journal: Information Sciences - Volume 329, 1 February 2016, Pages 52-72
نویسندگان
Anbo Meng, Zhuan Li, Hao Yin, Sizhe Chen, Zhuangzhi Guo,