عملکرد روش‌های بهینه‌سازی هوشمند در مسائل شناسایی سیستم IIR

نوع مقاله: مقاله پژوهشی فارسی

نویسندگان

دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند، بیرجند، ایران

چکیده

روش‌های بهینه‌سازی هوشمند با استفاده از تجربیات گذشته‌ی جمعیتی از عوامل جستجو، به‌طور مؤثر به کاوش و مرور فضای پاسخ می‌پردازند. این تکنیک‌های مبتنی بر هوش جمعی قادرند مسائل بهینه‌سازی پیچیده را با تعداد تکرار معین حل کنند. این مقاله به ارزیابی عملکرد گونه‌های متفاوتی از الگوریتم‌های رایج و قدرتمند بهینه‌سازی در مسأله شناسایی سیستم در جهت طراحی و مدل‌سازی بهینه فیلترهای دیجیتال پاسخ ضربه نامتناهی (IIR) می‌پردازد. روش‌های مفروض عبارتند از: الگوریتم‌های وراثتی (GA) و تکامل تفاضلی (DE) مبتنی بر نظریه تکامل در کنار شش الگوریتم هوش جمعیِ بهینه‌سازی گروه ذرات (PSO)، الگوریتم جستجوی گرانشی (GSA)، بهینه‌سازی سیستم صفحات شیبدار (IPO)، بهینه‌ساز مورچه‌گیر (ALO)، بهینه‌سازی آموزش و یادگیری (TLBO) و برای اولین بار از الگوریتم بهینه‌سازی بیوگرافی (BBO). در پژوهش حاضر، مسأله شناسایی سیستم IIR به‌عنوان یک تابع بهینه‌سازی تک‌هدفه فرض شده و به ازای دو مدل IIR آزمایشی و چالشی برای مدل-سازی با مرتبه معادل و مرتبه کاهش‌یافته مورد ارزیابی قرار می‌گیرد. برای ارزیابی بازدهی و عملکرد الگوریتم‌ها، نتایج در قالب شاخص‌های ضریب موفقیت (IoS) و درجه اطمینان (DoR) همراه با میانگین مربع خطا (MSE) مورد بررسی قرار می‌گیرد. همچنین اثر کاهش عوامل جستجو بر روی عملکرد الگوریتم‌ها مورد تحلیل قرار می‌گیرد. برآورد کلی نتایج تصدیق اثربخشی شاخص‌های ارزیابی مفروض و عملکرد مطلوب روش‌های پیشنهادی به‌ویژه به ازای الگوریتم‌های PSO، IPO و BBO از جهت مشخصات همگرایی، میانگین زمان اجرا، متوسط مقادیر برازندگی MSE و شاخص‌های IoS و DoR؛ الگوریتم‌های GA و GSA از جهت همگرایی، زمان اجرا و DoR؛ روش DE به‌جهت زمان اجرا؛ الگوریتم ALO به‌جهت متوسط MSE و الگوریتم TLBO از جهت مشخصات همگرایی، میانگین IoS و درصد DoR را نشان می‌دهد.

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