IJE TRANSACTIONS A: Basics Vol. 27, No. 1 (January 2014) 1-6   

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S. Ranjkesh
( Received: January 08, 2013 – Accepted: June 20, 2013 )

Abstract    In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of motion and search.The proposed algorithm along with the standard SFLA and two global and local versions of particle swarm optimization algorithm have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed algorithm has a very good performance.


Keywords    Frog Leaping Algorithm,Optimization, cellular learning automata.


چکیده    در این مقاله یک الگوریتم جدید که از ترکیب اتوماتای یادگیر سلولی و الگوریتم جهش قورباغهها (SFLA) حاصل میشود، برای بهینهسازی در محیطهای پیوسته و ایستا، پیشنهاد میگردد. در الگوریتم پیشنهادی هر ممپلکس از قورباغه ها در یک سلول از اتوماتای یادگیر سلولی قرار می گیرند. اتوماتای یادگیر موجود در هر سلول به عنوان مغز متفکر ممپلکس عمل می کند و استراتژی حرکت وجستجو را تعیین میکند. الگوریتم پیشنهادی به همراه SFLA استاندارد و دو نسخۀ سراسری و محلی الگوریتم بهینه سازی دستۀ ذرات در فضای 30 بُعدی بر روی پنج تابع شایستگی استاندارد آزمایش شده اند. نتایج آزمایشات نشان میدهند که الگوریتم پیشنهادی از کارایی بسیار مناسبی برخوردار است.


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