نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ، تهران، ایران.

3 گروه مدیریت صنعتی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی تهران، ایران.

10.22105/dmor.2021.276125.1333

چکیده

هدف:  در سال‌های اخیر، شاهد ظهور و گسترش الگوریتم­های فرا ابتکاری و استفاده از آن­ها جهت حل مسائل پیچیده، غیرخطی و ابعاد بالا بوده‌ایم. با توجه به اینکه الگوریتم­های فوق برای حل مسائل پیچیده و در حال تغییر دنیای واقعی به کار می­روند، دنیای الگوریتم­ها و طراحی آن­ها به شکل فزاینده‌ای پویا و رو به رشد بوده است. بنابراین، پیوسته شاهد به وجود آمدن الگوریتم‌های جدیدی هستیم. هدف از این تحقیق، ارائه یک الگوریتم فرا ابتکاری جدید به نام «الگوریتم بهینه‌سازی نظامی» می­باشد.
روش‌شناسی پژوهش: با الهام از عملیات­های نظامی الگوریتم پیشنهادی طراحی و ارائه گردید و پس از کدنویسی، توابع تست استاندارد و الگوریتم­های محک برای ارزیابی عملکرد آن تعیین و مشخص شدند.
یافته‎ها:  عملکرد الگوریتم پیشنهادی به‌وسیله 23 تابع تست استاندارد و با در نظر گرفتن شاخص­های «میانگین جواب­ها»، «میانگین زمان محاسباتی» و «زمان همگرایی» در مقایسه با هشت الگوریتم محک شامل: ژنتیک، ازدحام ذرات، کلونی زنبور مصنوعی، قورباغه جهنده، رقابت استعماری، گرگ خاکستری، بهینه­سازی وال و بهینه­سازی ملخ مورد ارزیابی و سنجش قرار گرفت. نتایج نشان‌دهنده عملکرد مطلوب الگوریتم پیشنهادی است.
اصالت/ارزش افزوده علمی:   در این مقاله، با الهام از عملیات­های نظامی الگوریتم جدیدی به نام الگوریتم بهینه­سازی نظامی (MOA) ارائه می­شود که مبتنی بر جمعیت است و بر اساس «جستجوی تصادفی»، «تقسیم فضای جواب به چند منطقه و تخصیص بخشی از جمعیت به هر منطقه»، «جستجوی سواره‌نظام» و «جستجوی پیاده‌نظام» عمل می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A new meta-heuristic algorithm: military optimization algorithm (MOA)

نویسندگان [English]

  • Hojatollah Rajabi Moshtaghi 1
  • Abbass Toloie-Eshlaghy 2
  • Mohammad Reza Motadel 3

1 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Industrial Management. Central Tehran Branch, Islamic Azad University, Tehran, Iran.

چکیده [English]

Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA).
 
Methodology: Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.
Findings: The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time".  The results show the excellent performance of the proposed algorithm.




Originality/Value: In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".  

کلیدواژه‌ها [English]

  • optimization
  • meta-heuristic algorithms
  • Military Optimization Algorithm
  • evolutionary algorithms
  • Swarm Algorithms
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