کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6903211 1446752 2017 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A self-adaptive multi-population based Jaya algorithm for engineering optimization
ترجمه فارسی عنوان
الگوریتم جیاا برای بهینه سازی مهندسی با استفاده از خودپردازنده چند جمعیت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
The robustness of the proposed SAMP-Jaya algorithm is tested on 15 CEC 2015 unconstrained benchmark problems in addition to 15 unconstrained and 10 constrained standard benchmark problems taken from the literature. The Friedman rank test is conducted in order to compare the performance of the algorithms. It has obtained first rank among six algorithms for 15 CEC 2015 unconstrained problems with the average scores of 1.4 and 1.9 for 10-dimension and 30-dimension problems respectively. Also, the proposed algorithm has obtained first rank for 15 unimodal and multimodal unconstrained benchmark problems with the average scores of 1.7667 and 2.2667 with 50000 and 200000 function evaluations respectively. The performance of the proposed algorithm is further compared with the other latest algorithms such as across neighborhood search (ANS) optimization algorithm, multi-population ensemble of mutation differential evolution (MEMDE), social learning particle swarm optimization algorithm (SL-PSO), competitive swarm optimizer (CSO) and it is found that the performance of the proposed algorithm is better in more than 65% cases. Furthermore, the proposed algorithm is used for solving a case study of the entropy generation minimization of a plate-fin heat exchanger (PFHE). It is found that the number of entropy generation units is reduced by 12.73%, 3.5% and 9.6% using the proposed algorithm as compared to the designs given by genetic algorithm (GA), particle swarm optimization (PSO) and cuckoo search algorithm (CSA) respectively. Thus the computational experiments have proved the effectiveness of the proposed algorithm for solving engineering optimization problems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Swarm and Evolutionary Computation - Volume 37, December 2017, Pages 1-26
نویسندگان
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