کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
172051 458518 2016 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design
ترجمه فارسی عنوان
یک الگوریتم تکاملی چند هدفه مبتنی بر بایگانی با پارتیشن بندی فضای جستجوی انطباق برای مقابله با مشکلات بهینه سازی گران قیمت: درخواست برای پردازش طراحی محیطی
کلمات کلیدی
بهینه سازی چند منظوره مبتنی بر شبیه سازی گران قیمت، الگوریتم تکاملی چند هدفه، اکتشافات بهبود همگرایی، سازگار با محیط زیست صنعتی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


• Optimal Pareto fronts were achieved with 200 and 300 function calls (2D, 3D problems).
• Convergence accuracy was improved by a factor of 50 for most test functions.
• Convergence improvements were attained for both unimodal and multimodal problems.
• Proposed algorithm outperformed ParEGO in both convergence accuracy and speed.

In eco-design, the integration of environmental aspects into the earliest stage of design is considered with the aim of reducing adverse environmental impacts throughout a product's life cycle. An eco-design problem is therefore multi-objective, where several objectives (environmental, economic, and technological) are to be simultaneously optimized.The optimization of industrial processes usually requires solving expensive multi-objective optimization problems (MOPs). Aiming to solve efficiently MOPs, with a limited computational budget, this paper proposes a new framework called AMOEA-MAP. The framework relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy, which provides an adaptive reticulation of the search space for a quick identification of optimal zones with less computational effort; and a bi-population evolutionary algorithm, tailored for expensive optimization problems.To ascertain its generality, the framework is first tested on several tough benchmarks. Its performance is subsequently validated on a real-world eco-design problem.

When dealing with expensive multi-objective optimization problems, the AMOEA-MAP algorithm (this work) allowed the granting of a better identification of globally optimal zones and a better distribution of optimal solutions in the optimal Pareto sets, compared to the ParEGO algorithm. In the following graphics, the comparisons have been made through several three-dimensional benchmarks (DTLZ1-6), where a limited computational budget of 300 function evaluations has been set for each run.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 87, 6 April 2016, Pages 95–110
نویسندگان
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