تنبیه: عملگری جدید برای کنترل فشار انتخاب و بهبود کارایی الگوریتم کپک مخاطی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه یزد، یزد، ایران

2 دانشیار گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه یزد، یزد، ایران

3 استادیار گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه یزد، یزد، ایران

10.22034/abmir.2025.22831.1104

چکیده

در این مقاله، الگوریتم کپک مخاطی (SMA) که از رفتار بیولوژیکی کپک‌های مخاطی الهام گرفته‌شده است، به‌عنوان روشی فراابتکاری قدرتمند برای حل مسائل پیچیده بهینه‌سازی بررسی و ارزیابی‌شده است. یکی از چالش‌های اصلی این الگوریتم، همگرایی زودرس ناشی از عدم کنترل فشار انتخاب است. به‌منظور رفع این مشکل، عملگر تنبیه ارائه‌شده است تا فشار انتخاب را کنترل کند وتنوع جمعیت را حفظ کند. این عملگر برخلاف روش‌های دیگر که فشار انتخاب را حین فرآیند محاسباتی الگوریتم کنترل نمی‌کنند، به‌صورت پویا رفتار خود را براساس شرایط فعلی الگوریتم تنظیم می‌کند. تا در مواقعی که ذرات دچار همگرایی زودرس می‌شوند، یک نیروی دافعه به آن‌ها اعمال می‌کند که از بهینه‌محلی رهایی یابند و کاوش بهتری در فضای جستجو انجام دهند. آزمایش‌های گسترده‌ای بر روی 23 تابع آزمایشی CEC2017، که شامل توابع مختلفی از قبیل تک‌حالته، چندحالته، ترکیبی و پیچیده است، انجام‌شده تا عملکرد عملگر پیشنهادی در شرایط پیچیده و متنوع ارزیابی شود. نتایج حاصل نشان می‌دهد که SMA ی بهبودیافته توانسته است در مقایسه با نسخه اصلی روی توابع تست استاندارد کارایی الگوریتم را 5/۳۵٪ بهبود دهد. نتایج شبیه‌سازی و آزمایش‌ها در این پژوهش نشان‌دهنده کارایی عملگر پیشنهادی در کنترل فشار انتخاب و درنتیجه کارایی بهتر الگوریتم کپک مخاطی بهبودیافته در حل مسائل بهینه‌سازی پیچیده است و می‌تواند در زمینه‌های گسترده‌ای از علوم و مهندسی به‌طور مؤثر و کارآمد استفاده شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Punishment: A new operator to control selection pressure and improve the efficiency of the slime mold algorithm

نویسندگان [English]

  • Arezoo Rahimi 1
  • Mohammad Farshi 2
  • Sepehr Ebrahimi Mood 3
1 MSc.Student, Department of Computer Science, Yazd University, Yazd, Iran
2 Associate Professor, Department of Computer Science, Yazd University, Yazd, Iran
3 Assistant Professor, Department of Computer Science, Yazd University, Yazd, Iran
چکیده [English]

In this paper, the Slime Mould Algorithm (SMA), inspired by the biological behavior of slime moulds, is examined and evaluated as a powerful metaheuristic method for solving complex optimization problems. One of the main challenges of this algorithm is premature convergence caused by the lack of control over selection pressure. To address this issue, a penalizing operator is proposed to regulate selection pressure and maintain population diversity. Unlike other methods that do not manage selection pressure during the computational process of the algorithm and make decisions regardless of the algorithm's current state, the proposed operator dynamically adjusts its behavior based on the algorithm's current conditions. When particles experience premature convergence, a repulsive force is applied to help them escape local optima and achieve better exploration of the search space. Extensive experiments were conducted on 23 benchmark functions from the CEC2017 suite, including various types such as unimodal, multimodal, hybrid, and complex functions, in order to evaluate the performance of the proposed operator under diverse and challenging conditions. The results show that the improved SMA achieved a 35.5% performance enhancement compared to the original version on standard test functions. The simulation results and experimental findings in this study demonstrate the effectiveness of the proposed operator in controlling selection pressure, leading to improved performance of the enhanced Slime Mould Algorithm in solving complex optimization problems. This makes it a practical and efficient solution for a wide range of applications in science and engineering.

کلیدواژه‌ها [English]

  • Slime mould algorithm
  • Selection pressure
  • Punishment operator
  • Optimization

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 2، شماره 2
اسفند 1403
صفحه 145-161

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