معرفی نسخه بهبودیافته الگوریتم خفاش

نوع مقاله : مقاله کامپیوتر

نویسندگان

1 گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی دانشگاه ملایر، ملایر، ایران

2 گروه مهندسی برق، موسسه آموزش عالی آفرینش علم گستر بروجرد، بروجرد، ایران

3 گروه مهندسی کامپیوتر، موسسه آموزش عالی آفرینش علم گستر بروجرد، بروجرد، ایران

چکیده

الگوریتم خفاش، نمونه‌ای از الگوریتم‌های فراابتکاری از خانوادۀ هوش جمعی است که براساس رفتار پژواک‌یابی خفاش بنا شده است. این الگوریتم تنوع راه‌حل را با استفاده از روش تنظیم فرکانس حفظ می‌‌کند که می‌تواند به سرعت و به‌صورت کارآمد از مرحلۀ اکتشاف به بهره‌برداری تغییر مکان دهد. بنابراین، هنگامی که به یک راه‌حل سریع و دقیق نیاز باشد، این الگوریتم به یک بهینه‌ساز کارآمد برای هر برنامۀ کاربردی تبدیل می‌شود. الگوریتم خفاش با وجود فواید زیاد و کاربردی، دارای معایبی نیز است. یکی از این معایب که باعث کاسته شدن کارایی آن می‌شود، به دام افتادن در بهینۀ محلی است. برای حل مشکل مذکور در این پژوهش موقعیت و سرعت جمعیت اولیه را به سه روش با فرمول‌های مختلف بروز کرده، این امر باعث می‌شود تا پاسخ نهایی مسئله در بهینه محلی به دام نیفتد و تنوع در جمعیت رخ دهد. در این مقاله عملکرد الگوریتم خفاش بهبودیافته روی 11 تابع هدف نمونه بررسی و با سایر الگوریتم‌های مشابه مقایسه شده است، که نهایتاً نتایج حاصل شده نشان از برتری و دقت این الگوریتم نسبت به نمونه‌های مشابه دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Proposing an Improved Version of the Bat Algorithm

نویسندگان [English]

  • Davar Giveki 1
  • Javad Ebrahimi 2
  • Maryam Sarshar 3
1 Department of Computer Engineering, Technical and Engineering Faculty of Malayer University, Malayer, Iran
2 Department of Electrical Engineering, Institute of Higher Education Afarinesh Alam Gostar Borujard, Borujard, Iran
3 Department of Computer Engineering, Institute of Higher Education Afarinesh Alam Gostar Borujard, Borujard, Iran
چکیده [English]

The bat algorithm is an example of meta-heuristic algorithms from the collective swarm intelligence, which is based on the echolocation behavior of bats. This algorithm preserves the diversity of the solution by using a frequency tuning method that can quickly and efficiently shift from exploration to exploitation. Therefore, when a fast and accurate solution is needed, this algorithm becomes an efficient optimizer for any application. Although the bat algorithm has many practical benefits, it also has some disadvantages. One of these disadvantages that reduces its efficiency is being trapped in the local optimum. To solve the mentioned problem in this research, the position and speed of the initial population is updated in three ways with different formulas, this makes the final answer of the problem not trapped in the local optimum and diversity occurs in the population. In this article, the performance of the improved bat algorithm on 11 sample objective functions has been investigated and compared with other similar algorithms, and finally the results show the superiority and accuracy of this algorithm compared to similar samples.

کلیدواژه‌ها [English]

  • Optimization
  • Swarm intelligence
  • Meta-Heuristic algorithm
  • Local optimum
  • Bat algorithm

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مدل سازی در مهندسی
دوره 22، شماره 79
در حال تکمیل شدن
دی 1403
صفحه 267-279

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سابقه مقاله

  • تاریخ دریافت: 14 بهمن 1402
  • تاریخ بازنگری: 09 اردیبهشت 1403
  • تاریخ پذیرش: 16 خرداد 1403

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