توسعه یک روش هوشمند خوشه‌بندی چندمعیاره مبتنی بر پرامتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار مدیریت صنعتی، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران.

2 استادیار مدیریت صنعتی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.

3 کارشناسی ارشد مدیریت صنعتی، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

در سال‌­های اخیر مسئله جدیدی با عنوان «خوشه‌­بندی چند­معیاره» ظهور کرده که هدف آن، دسته‌بندی گزینه­‌ها در گروه‌­های همگنی به نام خوشه با توجه به معیارهای ارزیابی متفاوت است. در ادامه پژوهش­‌های انجام­‌گرفته در مبانی نظری، پژوهش حاضر با ترکیب الگوریتم K- میانگین و تکنیک پرامتی، به­‌دنبال توسعه یک روش جدید خوشه­‌بندی چندمعیاره است. پارامترهای مسئله، پروفایل­‌های جدا­کننده خوشه‌­ها هستند که برای بهینه­‌سازی آن­ها از الگوریتم ژنتیک استفاده شده است. برای تنظیم پارامترهای ژنتیک نیز از روش تاگوچی استفاده می­‌شود. در این مدل­‌سازی، متغیرها در هر مرحله از به‌روزرسانی جواب­‌ها، با توجه به فاصله امتیاز جریان خالص خود از پروفایل­‌ها به نزدیک‌ترین خوشه تخصیص می­‌یابند. عملگر جهش نیز صرفاً زمانی اعمال می­‌شود که میزان شباهت کروموزوم­‌ها در هر جمعیت به حد خاصی برسد که این هوشمند­سازی موجب کاهش زمان محاسباتی شده است. درنهایت با اجرای روش پیشنهادی بر روی چند نمونه مسائل تصادفی مالی، عملکرد آن با سایر الگوریتم­‌های شناخته­‌شده خوشه­‌بندی مقایسه شده است. نتایج نشان می­‌دهد که روش پیشنهادی ضمن تعیین تعداد بهینه خوشه‌­ها، در مقایسه با سایر الگوریتم‌­ها، جواب­‌های دقیق­‌تری ارائه می‌­دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE

نویسندگان [English]

  • Amir Daneshvar 1
  • Mahdi Homayounfar 2
  • Ania Farahmandnejad 3
1 Assistant Professor in Industrial Management, Electronic Branch, Islamic Azad University, Tehran, Iran.
2 Assistant Professor in Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran.
3 M.A. in Management of Information Technology, Electronic Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

In recent years, a new issue called "multi-criteria clustering" has emerged that aims at grouping alternatives into homogeneous classes called clusters according to different evaluation criteria. Following the related studies in literature, by combining K-means algorithm and PROMETHEE technique, this paper aims to present a new multi-criteria clustering method. The parameters of the problem are the cluster separator profiles which genetic algorithm (GA) is used to optimize them. In the modeling process in each stage of updating responses, alternatives allocate to the nearest cluster according to the distance of their pure flow of privileges from the profiles. The mutation operator is only applied when the chromosomes’ similarity level in each population reaches to a certain level which this intelligence reduces the computation time. Finally, by simulating the proposed algorithm and some well-known clustering algorithms based on the several financial databases the efficiency of the algorithm compared to other algorithms. The results show the algorithm, in addition to determine the optimal number of clusters in comparison to other algorithms, also provides better results.

کلیدواژه‌ها [English]

  • Multi-Criteria Clustering
  • Genetic Algorithm
  • K-Means Algorithm
  • Silhouette Index
  • PROMETHEE
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