ادغام الگوریتم‌های بازار بورس، ملکه زنبور عسل و تکامل مختلط تصادفی برای بهینه‌سازی تابع چند متغیره

نوع مقاله : علمی-پژوهشی

نویسندگان

1 Department of Control Engineering, Faculty of Electrical and Computer Engineering, university of Tabriz

2 Department of Electronics, Carleton University, Ottawa, Canada

3 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

چکیده

در این مقاله، سه الگوریتم فراابتکاری شناخته‌شده شامل الگوریتم بازار بورس، الگوریتم تکامل پیچ درهم و الگوریتم زنبور ملکه به منظور ارائه سه الگوریتم تکاملی ترکیبی جدید با نام‌های EMA-QB، EMA-SCE و EMA-SCE-QB مورد بررسی قرار گرفته‌اند. به‌منظور تحلیل و ارزیابی کارایی و اثربخشی این الگوریتم‌های ترکیبی، عملکرد آن‌ها با الگوریتم‌های EMA، SCEو QB در حل 12 تابع محک با تعداد متغیرهای ۱۰، ۲۰، ۳۰ و ۵۰ مقایسه شده است. نتایج نشان می‌دهد که ترکیب الگوریتم‌ها منجر به بهبود عملکرد در جستجوی نقطه بهینه از نظر دقت و زمان شده است، به‌گونه‌ای که این بهبود با افزایش تعداد متغیرها ملموس‌تر می‌شود. در نهایت، مجموع زمان اجرای الگوریتم‌ها، کمینه مقدار توابع هدف، و تعداد تکرارهای لازم برای بهینه‌سازی تمامی توابع مورد بررسی، در قالب چهار نمودار برای هر تعداد متغیر به تصویر کشیده شده‌اند که نشان‌دهنده موفقیت الگوریتم‌های ترکیبی پیشنهادی است.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Integrating Exchange Market, Queen Bee, and Shuffled Complex Evolution Algorithms for Multi-variable Function Optimization

نویسندگان [English]

  • Mina Salim 1
  • Sima Hamedifar 2
  • Ali Asghar Lotfi 3
1 Department of Control Engineering, Faculty of Electrical and Computer Engineering, university of Tabriz
2 Department of Electronics, Carleton University, Ottawa, Canada
3 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

In this paper, three popular algorithms, including the Exchange Market Algorithm (EMA), the Shuffled Complex Evolution (SCE) algorithm, and the Queen Bee (QB) algorithm, are considered to propose three new hybrid evolutionary algorithms named EMA-QB, EMA-SCE, and EMA-SCE-QB. Then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of EMA, SCE, and QB algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. It is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. Finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms.

کلیدواژه‌ها [English]

  • Hybrid algorithm
  • Exchange market algorithm
  • Queen bee algorithm
  • Shuffled complex evolution

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