کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495308 862822 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization
ترجمه فارسی عنوان
آموزش و آزمایش یک الگوریتم تکاملی چند اپراتور خود سازگار برای بهینه سازی محدود
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• Measure the performance of SAMO-GA on a diverse set of constrained problems.
• Extend the idea of training and testing with EAs for solving COPs.
• The mean square error measure is used to quantify the results.
• The results provide interesting insights and a new way of choosing parameters.

Over the last two decades, many different evolutionary algorithms (EAs) have been introduced for solving constrained optimization problems (COPs). Due to the variability of the characteristics in different COPs, no single algorithm performs consistently over a range of practical problems. To design and refine an algorithm, numerous trial-and-error runs are often performed in order to choose a suitable search operator and the parameters. However, even by trial-and-error, one may not find an appropriate search operator and parameters. In this paper, we have applied the concept of training and testing with a self-adaptive multi-operator based evolutionary algorithm to find suitable parameters. The training and testing sets are decided based on the mathematical properties of 60 problems from two well-known specialized benchmark test sets. The experimental results provide interesting insights and a new way of choosing parameters.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 26, January 2015, Pages 515–522
نویسندگان
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