A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization
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الگوریتم‌های evolutionary از مکانیزم‌ها و عملیات ابتدایی برای حل مسئله استفاده می‌کنند و در طی یک سری از تکرارها به راه حل مناسب برای مسئله می‌رسند. این الگوریتم‌ها غالباً از یک جمعیت حاوی راه‌حل‌های تصادفی شروع می‌کنند و در طی هر مرحله تکرار سعی در بهتر کردن مجموعه راه‌حل‌ها دارند.
در آغاز کار تعدادی از اعضای جامعه به صورت تصادفی حدس زده شده، سپس تابع هدف برای هر یک از این اعضا محاسبه و نخستین نسل ایجاد خواهد شد. اگر هیچ‌یک از معیارهای خاتمه بهینه‌سازی دیده نشوند، ایجاد نسل جدید آغاز خواهد شد. اعضا بر حسب میزان شایستگی‌شان برای تولید نوزادها انتخاب می‌شوند. این افراد به عنوان والدین محسوب می‌شوند و بازترکیب نوزادها را تولید می‌نمایند. سپس تمامی نوزادها با یک مقدار معینی از احتمال، یعنی همان جهش، تغییر ژنتیکی می‌یابند. اکنون میزان شایستگی (برازندگی) نوزادان تعیین و در اجتماع جایگزین والدین شده و نسل جدید را ایجاد می‌نمایند. این چرخه آنقدر تکرار می‌شود تا یکی از معیارهای پایان بهینه‌سازی کسب شود. از جمله کاربردهای این الگوریتم ها : هوش مصنوعی، دانش‌های کاربردی: برق، مکانیک، صنایع، شیمی و غیره، سنتز و آزمون‌های سخت‌افزاری، طراحی و بهینه‌سازی فیلترهای دیجیتال و آنالوگ، استفاده در سیستم‌های چند پردازنده‌ای
در این صفحه، تعداد 239 مقاله انگلیسی از ژورنال ها و مجلات معتبر پایگاه ساینس دایرکت (ScienceDirect) درباره موضوع الگوریتم های تکاملی آرشیو شده است که شما می توانید مقالات مورد نظر خود را بر اساس سال انتشار، موضوع مقاله، وضعیت ترجمه و تعداد صفحات، انتخاب نموده و دانلود فرمایید.
در صورتی که مقاله لاتین مورد نظر شما تا کنون به زبان فارسی ترجمه نشده باشد، واحد ترجمه پایگاه ISI Articles با همکاری تنی چند از اساتید و مترجمان با سابقه، آمادگی دارد آن را در اسرع وقت و با کیفیت مطلوب برای شما ترجمه نماید.
در صورتی که مقاله لاتین مورد نظر شما تا کنون به زبان فارسی ترجمه نشده باشد، واحد ترجمه پایگاه ISI Articles با همکاری تنی چند از اساتید و مترجمان با سابقه، آمادگی دارد آن را در اسرع وقت و با کیفیت مطلوب برای شما ترجمه نماید.
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