کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6864616 | 1439546 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Objective reduction particle swarm optimizer based on maximal information coefficient for many-objective problems
ترجمه فارسی عنوان
بهینه ساز ذرات کاهش ذرات بر اساس حداکثر ضریب اطلاعات برای مشکلات بسیاری از اهداف
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کلمات کلیدی
ضریب اطلاعات حداکثر، بهینه سازی ذرات ذرات، کاهش هدف، بسیاری از مشکلات هدف،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
It is challenging to solve reducible many-objective problems due to difficulties caused by the unknown number of non-conflicting objectives. Objective reduction method is one of promising and efficient solutions in which two fundamental problems should be addressed: how to find the redundant objectives and which objectives should be selected or omitted. A novel objective reduction algorithm is proposed in this paper, named Maximal Information Coefficient based Multi-Objective Particle Swarm Optimizer (MIC-MOPSO). By a powerful MIC indicator, the algorithm could find hidden linear or nonlinear relationships between two objectives. Another indicator, the change rate of non-dominated population, is used to judge whether there exist non-conflicting objectives or not. An effective way to rapidly select the retained objectives is also developed based on these two indicators. Tested by a series of benchmark experiments and a real industrial optimization problem, the results show that our approach significantly improve the performance on both reducible and irreducible many-objective problems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 281, 15 March 2018, Pages 1-11
Journal: Neurocomputing - Volume 281, 15 March 2018, Pages 1-11
نویسندگان
Liang Yi, He Wangli, Zhong Weimin, Qian Feng,