بهره‌گیری از رویکردهای جدید بهینه‌سازی هوشمند فراابتکاری مبتنی بر هوش مصنوعی در طراحی سیستم‌های ناوبری INS
محورهای موضوعی : مهندسی برق و کامپیوترعلی محمدی 1 * , فرید شیخ الاسلام 2 , مهدی امامی 3
1 - دانشگاه صنعتی اصفهان،دانشكده مهندسی برق و كامپیوتر
2 - دانشگاه صنعتی اصفهان ،دانشكده مهندسی برق و كامپیوتر
3 - دانشگاه یزد،دانشكده مهندسی مکانیک
کلید واژه: بهینه‌سازی هوشمند, الگوریتم‌های فراابتکاری, محاسبات نرم, ناوبری تلفیقی INS/GNSS, بهینه‌سازی سیستم صفحات شیب‌دار,
چکیده مقاله :
به کارگیری تکنیک‌های محاسبات نرم در علوم مهندسی حجم زیادی از پژوهش‌ها را شامل شده است. از جمله این مسایل می‌توان به طراحی و بهینه‌سازی سیستم‌های ناوبری جهت استفاده در سیستم‌های حمل‌ونقل زمینی، دریایی و هوایی اشاره کرد. از این رو در این پژوهش سعی در بهره‌گیری از رویکردهای جدید بهینه‌سازی هوشمند فراابتکاری مبتنی بر هوش مصنوعی در جهت طراحی سامانه‌های ناوبری تلفیقی می‌باشد. برای این منظور از نسخه جدید الگوریتم بهینه‌سازی سیستم صفحات شیب‌دار به همراه چند نسخه دیگر آن در کنار دو روش مرسوم الگوریتم زیستی و بهینه‌سازی ازدحام ذرات استفاده شده است. ملاحظات بر روی یک مسأله INS/GNSS با ماژول‌های اندازه‌گیری اینرسی IMU MEMS انجام شدند. ماتریس‌های کواریانس نویز فرایند و اندازه‌گیری به عنوان متغیرهای طراحی و مجموع میانگین مربعات خطا به عنوان تابع هدف در قالب یک مسأله کمینه‌سازی تک‌هدفه در نظر گرفته شده‌اند. خروجی‌ها بر حسب شاخص‌های آماری و عملکردی نظیر زمان اجرا، برازندگی، همگرایی‌ها، دقت سرعت‌های زاویه‌ای، طول و عرض جغرافیایی، بلندی، Roll، Pitch، Yaw و مسیریابی به همراه رتبه‌بندی الگوریتم‌ها ارائه شدند. برایند کلی نتایج حکایت از عملکرد موفق و برتری نسبی روش های IPO و IIPO نسبت به رقبا و همچنین کارکرد قابل رقابت الگوریتم های پیشنهادی در قیاس با حجم ملاحظات و محاسبات مسأله مفروض دارد.
Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantage of novel approaches of intelligent metaheuristic optimization for designing integrated navigation systems. For this purpose, the inclined planes system optimization algorithm with several modified and new versions have been used along with two well-known methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS inertia measurement modules. Process and measurement noise covariance matrices are considered as design variables and the sum of mean-squares-error as an objective function in the form of a single-objective minimization problem. Outputs are presented in terms of statistical and performance indicators such as runtime, fitness, convergences, angular-velocity accuracy, latitude, longitude, altitude, roll, pitch, yaw, and routing along with the ranking of algorithms. The overall assessment indicated the correctness of the performance and the relative superiority of the IPO and IIPO over the competitors and competitive performance of the assumed algorithms in comparison with the volume of considerations and calculations of the base problem.
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