|
|
International Journal of Supply and Operations Management، جلد ۲، شماره ۱، صفحات ۵۶۹-۵۹۴
|
|
|
| عنوان فارسی |
|
|
| چکیده فارسی مقاله |
|
|
| کلیدواژههای فارسی مقاله |
|
|
| عنوان انگلیسی |
An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants |
|
| چکیده انگلیسی مقاله |
Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration. |
|
| کلیدواژههای انگلیسی مقاله |
|
|
| نویسندگان مقاله |
| ellips masehian
tarbiat modares university, teahran, iran
سازمان اصلی تایید شده: دانشگاه تربیت مدرس (Tarbiat modares university)
| vahid eghbal akhlaghi
middle east technical university, ankara, turkey
| hossein akbaripour
tarbiat modares university, teahran, iran
سازمان اصلی تایید شده: دانشگاه تربیت مدرس (Tarbiat modares university)
| davoud sedighizadeh
islamic azad university, saveh branch, saveh, iran
سازمان اصلی تایید شده: دانشگاه آزاد اسلامی علوم و تحقیقات (Islamic azad university science and research branch)
|
|
| نشانی اینترنتی |
http://system.khu.ac.ir/ijsom/browse.php?a_code=A-10-100-41&slc_lang=en&sid=en |
| فایل مقاله |
فایلی برای مقاله ذخیره نشده است |
| کد مقاله (doi) |
|
| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Artificial intelligence & expert system |
| نوع مقاله منتشر شده |
Research paper |
|
|
|
برگشت به:
صفحه اول پایگاه |
نسخه مرتبط |
نشریه مرتبط |
فهرست نشریات
|
