کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946116 | 1439268 | 2017 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cooperative two-engine multi-objective bee foraging algorithm with reinforcement learning
ترجمه فارسی عنوان
الگوریتم تکثیر زنبور عسل دو موتوره با آموزش تقویت کننده تعاونی
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کلمات کلیدی
زنبور عسل، بهینه سازی چند هدفه، شاخص پارتو،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper proposes a novel multi-objective bee foraging algorithm (MOBFA) based on two-engine co-evolution mechanism for solving multi-objective optimization problems. The proposed MOBFA aims to handle the convergence and diversity separately via evolving two cooperative search engines with different evolution rules. Specifically, in the colony-level interaction, the primary concept is to first assign two different performance evaluation principles (i.e., Pareto-based measure and indicator-based measure) to the two engines for evolving each archive respectively, and then use the comprehensive learning mechanism over the two archives to boost the population diversity. In the individual-level foraging, the neighbor-discount-information (NDI) learning based on reinforcement learning (RL) is integrated into the single-objective searching to adjust the flight trajectories of foraging bee. By testing on a suit of benchmark functions, the proposed MOBFA is verified experimentally to be superior or at least comparable to its competitors in terms of two commonly used metrics IGD and SPREAD.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 133, 1 October 2017, Pages 278-293
Journal: Knowledge-Based Systems - Volume 133, 1 October 2017, Pages 278-293
نویسندگان
Ma Lianbo, Cheng Shi, Wang Xingwei, Huang Min, Shen Hai, He Xiaoxian, Shi Yuhui,