کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6903154 | 1446750 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A decomposition-based multi-objective evolutionary algorithm with quality indicator
ترجمه فارسی عنوان
الگوریتم تکاملی چند هدفه با شاخص تجزیه و تحلیل بر اساس تجزیه
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کلمات کلیدی
محاسبات تکاملی، بهینه سازی چند هدفه، تنوع الگوریتم، تجزیه، شاخص مبتنی بر،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Swarm and Evolutionary Computation - Volume 39, April 2018, Pages 339-355
Journal: Swarm and Evolutionary Computation - Volume 39, April 2018, Pages 339-355
نویسندگان
Jianping Luo, Yun Yang, Xia Li, Qiqi Liu, Minrong Chen, Kaizhou Gao,