مقاله انگلیسی الگوریتم تکامل افتراقی دینامیکی چند هدفه و کاربرد آن در مهندسی شیمی
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مقاله انگلیسی الگوریتم تکامل افتراقی دینامیکی چند هدفه و کاربرد آن در مهندسی شیمی

عنوان فارسی مقاله: الگوریتم تکامل افتراقی دینامیکی چند هدفه و کاربرد آن در مهندسی شیمی
عنوان انگلیسی مقاله: A self-adaptive multi-objective dynamic differential evolution algorithm and its application in chemical engineering
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی شیمی
گرایش های تحصیلی مرتبط: کنترل، طراحی فرآیند
کلمات کلیدی فارسی: بهینه سازی چند هدفه ، تکامل افتراقی پویا ، سازگاری پارامتر ، مسئله کنترل بهینه ، فرآیندهای شیمیایی و بیوشیمیایی
کلمات کلیدی انگلیسی: Multi-objective optimization, Dynamic differential evolution, Parameter adaptation, Optimal control problem, Chemical and biochemical processes
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2021.107317
دانشگاه: Tianjin University, Tianjin, PR China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 5.472 در سال 2020
شاخص H_index: 124 در سال 2021
شاخص SJR: 1.405 در سال 2020
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E15390
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
نوع رفرنس دهی: vancouver
فهرست انگلیسی مطالب

Highlights


Abstract


Keywords


Nomenclature


1. Introduction


2. The proposed SA-MODDE algorithm


3. Numerical experiments


4. Chemical engineering processes optimization


5. Conclusion


CRediT authorship contribution statement


Declaration of Competing Interest


Appendix A. Supplementary data


References

نمونه متن انگلیسی مقاله

Abstract


This paper proposes a new multi-objective dynamic differential evolution algorithm with parameter self-adaptive strategies, named SA-MODDE. All components of the algorithm are synergically designed to reach its full potential, containing parental selection, mutation strategy, parameter setting, survival selection, constraint handling, and termination criteria. The improvement measures emphasize exploiting Pareto dominance information more efficiently. Particularly, parameter adaptation schemes are introduced based on both prior knowledges of current individual and feedback information on previous promising solutions, and their effectiveness is validated by comparison with three fixed-parameter combinations. Extensive numerical tests are conducted on multiple test suites with five state-of-the-art peer competitors. The statistical results demonstrated that the SA-MODDE exhibits good proximity and diversity in dealing with benchmark functions with various characteristics. Three industrial (bio)chemical processes, including two optimal control and one reformulated constrained tri-objective, are investigated to show the feasibility and robustness of the SA-MODDE.


 


1. Introduction

Engineering problems always require the simultaneous optimization of several competing objectives of interests. So far, multi-objective optimization (MOO) has been an active research field in process systems engineering [1,2]. Particularly, various multi-objective evolutionary algorithms (MOEAs), such as NSGAII, GDE3, and MOPSO, have been widely used to solve both academic and industrial MOO problems [3–5]. Usually, MOEAs have two main advantages: (1) As many diverse non-dominated solutions as possible can be found in a single run; (2) Various types of MOO problems can be handled without assumptions on objective functions and their mathematical characteristics [6].

The algorithm structure and search operator jointly affect the performance of MOEAs. The algorithm structure can be classified into two main categories: Pareto-based [3,7] and decompositionbased [8,9]. The former provides detailed Pareto dominance information of the population to facilitate individual comparison. The latter decomposes MOO problems into a set of scalar aggregation subproblems, each of which is optimized using the current information from neighboring subproblems. The two methods have their own advantages on different types of problems and are considered to be evenly matched [10]. In terms of search operator, differential evolution (DE) is simple to implement with only a few control parameters, i.e., scale factor (F ) and crossover rate (CR). Except for multi-objective DE (MODE) algorithms, many classic MOEAs also replaced the original evolutionary operators with DE and their performance was significantly enhanced, such as NSGAII-DE, SPEA2-DE, IBEA-DE, and MOEA/D-DE [11,12]. Through updates by dynamic population rather than generation to generation, Qing [13] presented the dynamic DE (DDE) operator, superior in efficiency, robustness, and storage requirements to the conventional DE. That is, each new individual that performs better than or similar to the corresponding old counterpart will immediately participate in the current population to provide information for subsequent evolution. This makes DDE more responsive to changes in population status. Despite of researches on multi-objective DDE (MODDE) algorithms [14,15], it is still very inadequate compared to MODE. Herein, we propose several improvement measures on the MODDE under Pareto-based structure. For convenience, the background of MOO problems and DDE operators are given in Supplementary Materials.

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