کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6896786 | 1446007 | 2015 | 53 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Preference-inspired co-evolutionary algorithms using weight vectors
ترجمه فارسی عنوان
الگوریتم های هماهنگ تکاملی با الگوریتم های ترجیحی با استفاده از بردارهای وزن
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کلمات کلیدی
الگوریتمهای تکاملی، بهینه سازی چند هدفه، بسیاری از هدف، همکاری تکامل، وزنه
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
Decomposition based algorithms perform well when a suitable set of weights are provided; however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowledge about the geometry of the problem. This study proposes a novel algorithm called preference-inspired co-evolutionary algorithm using weights (PICEA-w) in which weights are co-evolved with candidate solutions during the search process. The co-evolution enables suitable weights to be constructed adaptively during the optimisation process, thus guiding candidate solutions towards the Pareto optimal front effectively. The benefits of co-evolution are demonstrated by comparing PICEA-w against other leading decomposition based algorithms that use random, evenly distributed and adaptive weights on a set of problems encompassing the range of problem geometries likely to be seen in practice, including simultaneous optimisation of up to seven conflicting objectives. Experimental results show that PICEA-w outperforms the comparison algorithms for most of the problems and is less sensitive to the problem geometry.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 243, Issue 2, 1 June 2015, Pages 423-441
Journal: European Journal of Operational Research - Volume 243, Issue 2, 1 June 2015, Pages 423-441
نویسندگان
Rui Wang, Robin C. Purshouse, Peter J. Fleming,