بهبود الگوریتم اثر نیلوفر آبی مبتنی بر سیستم استنتاج فازی
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
الهام دلیری نیا
1
,
مهرداد جلالی
2
,
مهدی یعقوبی
3
,
حمید طباطبایی
4
1 - دانشگاه ازاد اسلامی واحد مشهد
2 - گروه کامپیوتر دانشگاه ازاد اسلامی واحد مشهد
3 - Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
4 - Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran
کلید واژه: الگوریتم اثر نیلوفر آبی, بهینه سازی, سیستم استنتاج فازی, الگوریتم سنجاقک, الگوریتم تکاملی,
چکیده مقاله :
الگوریتم اثر نیلوفر آبی در سال 2024 ارائه شده است و برگرفته شده از گرده افشانی و حرکت بروی برگ‌های گل نیلوفر آبی می‌باشد. در این الگوریتم از مفاهیم هوش جمعی ایستا و پویا در قالب حرکت سنجاقک‌ها استفاده شده که قدرت فرایند اکتشافی را در الگوریتم افزایش داده و همچنین قدرت استخراج با گرده افشانی محلی و حرکت آب بروی برگ‌های گل نیلوفر آبی در نظر گرفته شده است. اما در این الگوریتم مکانیسم دقیقی برای کنترل پارامترهای مهم در فرآیند استخراج و اکتشاف در نظر گرفته نشده است و حرکات سنجاقک‌ها در همه شرایط بصورت تصادفی تعریف می‌شود و بدین خاطر، دقت و سرعت همگرایی این الگوریتم بهینه سازی، کاهش می‌یابد. در این مقاله یک سیستم استنتاج فازی در حرکت سنجاقک‌ها ارائه شده تا دقت و سرعت همگرایی این الگوریتم با کنترل شعاع همسایگی، حرکت همترازی و انسجام افزایش یابد. نتایج بدست آمده الگوریتم پایه گل نیلوفر آبی در مقایسه با الگوریتم پیشنهادی بروی 12 تابعتست در ابعاد بالا (50 بعد)، نشان داد که روش فازی الگوریتم گل نیلوفر آبی دارای بیش از 49 درصد بهبود در دقت همگرایی و بیش از 9 درصد بهبود در سرعت همگرایی است.
The Water Lily Effect Algorithm, introduced in 2024, is inspired by the processes of pollination and movement on water lily leaves. It utilizes concepts of static and dynamic swarm intelligence, modeled through dragonfly movements, to enhance the algorithm's exploratory capabilities. Additionally, local pollination and water movement on lily leaves contribute to its extraction efficiency. However, the algorithm lacks a precise mechanism for managing key parameters during exploration and extraction, with dragonfly movements being randomly defined in all scenarios. This limitation reduces both the accuracy and convergence speed of the optimization process.
This study proposes integrating a fuzzy inference system into the dragonfly movement mechanism to address these issues. By controlling the neighborhood radius, alignment, and cohesion movements, the algorithm achieves improved performance. When tested on 12 high-dimensional benchmark functions (50 dimensions), the fuzzy-enhanced Water Lily Algorithm showed a more than 49% improvement in convergence accuracy and over a 9% increase in convergence speed compared to the original algorithm.
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