ارائه یک نسخه بهبودیافته از الگوریتم ژنتیک مبتنی بر راهکار خودسازمان‌دهی بحرانی و حافظه گوسی برای حل مسائل بهینه‌سازی پویا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 باشگاه پژوهشگران و نخبگان، دانشگاه آزاد اسلامی واحد یاسوج، کهگیلویه و بویراحمد، ایران

2 دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت، تهران، ایران

3 دانشکده مهندسی کامپیوتر، دانشگاه آزاد اسلامی، نورآباد ممسنی، فارس، ایران

4 دانشکده فنی و مهندسی، گروه مهندسی برق و کامپیوتر، دانشگاه یاسوج، یاسوج، ایران

چکیده

از آن‌جایی‌که اجزای پویا، همراه با محدویت‌های غیرخطی و اهداف متعدد، یکی از خصوصیت‌هایی است که به‌طور مکرر در مسائل دنیای واقعی ظاهر می‌شود و از طرف دیگر زمان زیادی است که محاسبات تکاملی وارد حوزه کاربردهای صنعتی شده است (به خصوص به‌علت توانایی آن‎ها در مواجهه با محیط‎‌های چندهدفه و غیرخطی) انتظار می‌‎رود که توجه به این زمینه در جامعه علمی رشد پیدا کند. هدف این مقاله، امکان طراحی پروتکل‎‌های الهام گرفته از طبیعت در الگوریتم ژنتیک است که روی بهینه‎‌سازی در محیط‎‌های پویا موثر باشد، در حالی‌‎که پیچیدگی الگوریتم را حفظ کند و تغییرات در فضای مسئله به‌‎صورت دوره‎ای رخ دهد. در این مقاله، یک الگوریتم ژنتیک بهبودیافته (خودسازمانده بحرانی) مبتنی بر حافظه برای حل مسائل بهینه‎‌سازی پویا ارائه شده است. در الگوریتم ارائه‌‎شده، از یک عملگر جهش خودسازمانده استفاده شده است. این عملگر جهش می‎‌تواند نرخ‎‌های جهش خودتنظیم‌‎شونده را با یک توزیع ویژه بر مبنای مدل تپه شنی انجام دهد که این برای بهینه‌‎سازی پویا مناسب است. اگر تغییرات به‌‎صورت دوره‎ای رخ دهند، به‌‎طور معمول استفاده از اطلاعات گذشته اجازه می‎‌دهد الگوریتم به ‎سرعت بعد از تغییر محیط به سازگاری در شرایط محیطی جدید برسد. ایده مورد نظر در این زمینه، استفاده از یک حافظه می‎‌باشد. یکی از چالش‎‌های اساسی در به‎‌کارگیری حافظه، تنوع می‎‌باشد. برای افزایش سطح تنوع از یک حافظه تخمبن تراکم با خوشه‌بندی گاوسی استفاده شده است. همچنین از راهکاری برای جایگزینی و بازیابی در حافظه استفاده شده است. در طرح پیشنهادی ابتدا جهش خودسازمانده بحرانی جدید، با سایر الگوریتم‌‎های ژنتیک ارائه شده توسط سایر محققین ترکیب شده و نتایج حاصل شده نشان می‎‌دهد که این روش توانسته به‎‌کرات سایر الگوریتم‎‌های ژنتیک را برای محیط‎‌های پویا بهبود بخشد. در نهایت روش پیشنهادی این مقاله که ترکیب خودسازماندهی بحرانی جدید با حافظه تخمین تراکم گوسی است ارائه شده است. نتایج این روش بر روی مسائل محک مختلف با عنوان توابع تله پویا (Royal road، One Max و Deceptive)، آزمایش شده ونتایج حاصله حاکی از کارایی بیشتر روش پیشنهادی است.

کلیدواژه‌ها


عنوان مقاله [English]

Improved Genetic Algorithm Based on Critical Self-Organization and Gaussian Memory for Solving Dynamic Optimization Problems

نویسندگان [English]

  • Majid Mohammadpour 1
  • Behrooz Minaei 2
  • Hamid Parvin 3
  • Kyvan Rahimizadeh 4
1 Researchers and Elites Club, Islamic Azad University, Yasuj Branch, Kohgiluyeh and Boyerahmad, Iran
2 Faculty of Computer Engineering, University of Science and Technology, Tehran, Iran
3 Faculty of Computer Engineering, Islamic Azad University, Noorabad Mamasani, Fars, Iran
4 Faculty of Engineering, Department of Electrical and Computer Engineering, Yasouj University, Yasouj, Iran
چکیده [English]

Dynamic components, nonlinear limitation, and multi-objectives are characteristics that we face in the real world. On the other hand, evolutionary algorithms, thanks to their capability in management of multi-objective and non-linear environments, have been applied to industrial applications. Inspired by nature, this study tries to design optimization protocols in the genetic algorithm with keeping the algorithm complexity and periodic variation in the problem space. In other words, this study proposes an optimized memory-based genetic algorithm to solve dynamic optimization problems. A new self-organized mutation operator based on the sandhill model is used in this algorithm. The operator can predict self-regulated mutation rates based on the sandhill distribution model. If the variations occur periodically, the past data helps the algorithm reaches consistency with the environment after the environment change. The idea is switching to a new situation based on the memory. One of the issues of using memory is diversity. To this end, a density prediction memory with the gaussian cluster was is used. Moreover, an approach is used for information replacement and retrieval. In our proposed method, the self-organized mutation is composed to the genetic algorithms have been proposed. Results show the proposed method could improve the genetic algorithms for dynamic environments. Moreover, the proposed method was tested using different benchmark functions such as royal road, one max, and deceptive. 

کلیدواژه‌ها [English]

  • Optimization
  • Genetic algorithm
  • Dynamic environment
  • memory
  • Clustering
  • density
  • Estimation
  • Crossover
  • Mutation
  • Self-organizing
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