الگوریتم رقابت استعماری آشوبی متعامد اصلاح شده و بکارگیری آن در بهبود بازشناسی الگو در شبکۀ عصبی پرسپترون¬های چند لایه
محورهای موضوعی : عمومى
پیمان معلم
1
(هیات علمی گروه مهندسی برق)
مهرداد صادقی حریری
2
(دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران)
مهدی هاشمی
3
(موسسه آموزش عالی پیام گلپایگان، دانشکده مهندسی برق)
کلید واژه: الگوریتم رقابت استعماری آشوبی متعامد, شبکه عصبی پرسپترون چند لایه, طبقه¬بندی داده,
چکیده مقاله :
علی رغم موفقیت الگوریتم رقابت استعماری (ICA) در حل مسائل بهینه سازی، این الگوریتم کماکان از به دام افتادن مکرر در کمینه محلی و سرعت پایین همگرایی رنج می برد. در این مقاله، نسخۀ جدیدی از این الگوریتم، به نام رقابت استعماری آشوبی متعامد اصلاح شده (COICA)، پیشنهاد می شود. در سیاست جذب نسخه پیشنهادی، هرمستعمره از طریق تعریف بردار متعامد نوینی، فضای حرکت به سمت استعمارگر را جستجو می کند. همچنین احتمال انتخاب امپراطوری های قدرتمند، از طریق تابع توزیع بولتزمان تعریف شده و عمل انتخاب از طریق روش چرخ رولت انجام گرفته است. از الگوریتم پیشنهادی برای آموزش شبکه عصبی پرسپترون چند لایه (MLP) جهت طبقه بندی مجموعه داده های استاندارد، از جمله یونسفر و سونار استفاده شده است. برای ارزیابی عملکرد این الگوریتم و بررسی میزان تعمیم پذیری شبکه عصبی آموزش ديده با نسخه پيشنهادی، از روش اعتبارسنجی متقابل K-Fold استفاده شده است. نتایج بدست آمده از شبیه سازی ها، کاهش خطای آموزش شبکه و همچنین بهبود تعمیم پذیری الگوریتم پیشنهادی را تایید می کند.
Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm.
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