الگوریتم ترکیبی بر اساس الگوریتم‌های تکامل تفاضلی، ایمنی بدن و ازدحام ذرات برای انتخاب پارامترهای بهینه در فرزکاری

پناهی زاده, ولی اله; عبداله زاده, حسین

doi:10.48301/kssa.2022.321254.1914

الگوریتم ترکیبی بر اساس الگوریتم‌های تکامل تفاضلی، ایمنی بدن و ازدحام ذرات برای انتخاب پارامترهای بهینه در فرزکاری

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

¹ استادیار، دانشکده مهندسی مکانیک، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.

² دانشجوی دکتری، دانشکده مهندسی مکانیک، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.

10.48301/kssa.2022.321254.1914

چکیده

این مقاله با استفاده از درهم‌آمیختن الگوریتم بدن، برای بهینه‌سازی، شیوه‌ای ترکیبی و نوآورانه ارائه‌ کرده ‌است، سپس در مرحله جهش با استفاده از الگوریتم ایمنی بدن از الگوریتم ازدحام ذرات (PSO) برای تعیین مقدار ضریب جهش کمک ‌گرفته‌ است. از این‌رو الگوریتم مذکور با یک روش هم‌تکاملی خود پس از اصلاح، ضریب جهش بهینه را جست‌وجو می‌کند. مدل ارائه‌شده بر روی یک فرایند مطالعاتی فرزکاری آزمایش‌ شده ‌است تا تأثیر آن بر روی بهینه‌سازی پارامترهای فرزکاری مشخص‌ شود. نتایج به‌دست‌آمده با الگوریتم کلونی مورچهها، ایمنی بدن، الگوریتم ایمنی بدن ترکیبی، الگوریتم ژنتیک، روش ترکیبی تفاضل تکاملی با ایمنی بدن و مقادیر پیشنهادی هندبوک‌های ماشین‌کاری مقایسه ‌شده‌ است. با توجه به این مقایسه میزان بهبود پاسخهای ارائه ‌شده نسبت به روش‌های الگوریتم کلونی مورچهها 7/5٪، ایمنی بدن 5/4٪، الگوریتم ایمنی بدن ترکیبی 3٪، الگوریتم ژنتیک 5/8٪، روش ترکیبی تفاضل تکاملی با ایمنی بدن 2٪ و مقادیر پیشنهادی هندبوک‌های ماشین‌کاری 300٪ بوده ‌است.

کلیدواژه‌ها

20.1001.1.23829796.1401.19.3.8.8

موضوعات

ساخت و تولید

عنوان مقاله [English]

A Hybrid Algorithm Based on Differential Evolution, Artificial Immune System and Particle Swarm Algorithms for Selection of Optimal Machining Parameters in Milling Operations

نویسندگان [English]

Valiollah Panahizadeh ¹
Hossein Abdolahzadeh ²

¹ Assistant Professor, Faculty of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

² PhD Student, Faculty of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

چکیده [English]

This paper presents a combination of innovative methods for optimization using the integration of the evolutionary difference algorithm (DE) and the recipient's version of the immune system algorithm. In this paper, at the mutation stage, the particle swarm algorithm (PSO) was used to determine the value of the mutation coefficient using the immune system algorithm. Therefore, the algorithm modified its evolutionary method and searched for the optimal jump coefficient. The proposed model was tested on milling operations to determine its effect on the optimization of milling parameters. The results of the hybrid approach for the case study were compared with those of ant colony algorithm, body immunity, hybrid immune algorithm, genetic algorithm, HDRE approach, and the proposed values of machining handbooks. According to this comparison, the development ratio between the proposed algorithm and other approaches were as follows: ant colony algorithm was 5.7%, immune algorithm was 4.5%, hybrid immune algorithm was 3%, genetic algorithm was 8.5%, HDRE approach was 2% and handbook recommendation was 300%.

کلیدواژه‌ها [English]

Immune system Differential evolution Particle swarm algorithm Milling Co
evolutionary algorithm

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