Application of Classical Bird Swarm Learning Algorithm as a Method of Optimization in Nanotechnology Systems

Subject Areas : Journal of Optoelectronical Nanostructures

Abdorreza Asrar ¹ , Milad Yasrebi ²

1 - Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran
2 - Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran

Received: 2020-05-18 Accepted : 2020-07-14 Published : 2021-01-01

Keywords: Optimization, Nanotechnology, Quantum, Swarm Algorithm, Cost, Speed, Particle, Standard Deviation,

Abstract :

شکی نیست که فناوری نانو نقش عمده ای در
فناوری آینده ما خواهد داشت. علوم کامپیوتر فرصت های بیشتری برای
سیستم های کوانتومی و فناوری نانو فراهم می کند. تکنیک های محاسبات نرم مانند هوش انبوه ، می
توانند سیستم هایی با ویژگی های ظهور مطلوب را قادر سازند. بهینه سازی یک
فعالیت مهم و تعیین کننده در طراحی سازه است. نیاز ارزان در حافظه و
محاسبات با عوامل مستقل نانومتری که توانایی آنها ممکن است
توسط اندازه آنها محدود شود مناسب است. برای اعمال در کنترل نانوربات ، اصلاح الگوریتم PSO
مورد نیاز است. با استفاده از رفتار یادگیری شرطی سازی کلاسیک پرندگان در این مقاله ، ذرات
یاد می گیرند که یک رفتار طبیعی شرطی را نسبت به محرک بی قید و شرط انجام دهند.
ذرات موجود در فضای مسئله به چند دسته تقسیم می شوند و اگر ذره
ای تنوع رده خود را در سطح پایین پیدا کند ، سعی می کند به سمت بهترین
تجربه شخصی خود حرکت کند. ما همچنین از ایده حساسیت پرندگان به فضایی که
آنها در آن پرواز می کنند استفاده کردیم و سعی کردیم ذرات را در فضاهای نامناسب با سرعت بیشتری حرکت دهیم تا
از فضاها خارج شوند. برعکس ، ما سرعت ذرات را در
فضاهای ارزشمند کاهش می دهیم تا بیشتر جستجو کنیم. روش پیشنهادی در
نرم افزار MATLAB پیاده سازی شده و با نتایج مشابه مقایسه شده است. نشان داده شد که روش پیشنهادی
بدون توجه به عملکردهای غیر تعیین کننده یا
شرایط تصادفی ، راه حل خوبی برای مسئله پیدا می کند .

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