کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495254 862821 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight
ترجمه فارسی عنوان
الگوریتم بهینه سازی ذرات اولیه با الگوریتم بهینه سازی ذرات ناسازگار با وزن غیرانسانی بار متغیر غیر خطی مرتبه بالا
کلمات کلیدی
توالی کم اختلاف، بهینه سازی ذرات ذرات، تابع غیرخطی مرتبه 1 / π2، وزن غیردارانه کاهش غیرمستقیم، ضریب شتاب ثابت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• Initial population particles are generated by the Halton sequence to fill the search space efficiently.
• 1/π2 order nonlinear function is selected to adjust the time-varying inertia weight.
• Two acceleration coefficients are set to be constants.
• Performance of the proposed method is tested by a set of well-known benchmark optimization problems.

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. The most important features of the PSO are easy implementation and few adjustable parameters. A novel PSO method called LHNPSO, with low-discrepancy sequence initialized particles and high-order (1/π2) nonlinear time-varying inertia weight and constant acceleration coefficients, is proposed in this paper. The initial population particles are generated by using the Halton sequence to fill the search space efficiently. Nonlinear functions with orders varied within big ranges are employed to adjust the inertial weight, cognitive and social parameters. Based on the sensitivity analysis of PSO performance to the changes of the orders of these nonlinear functions, 1/π2 order nonlinear function is selected to adjust the time-varying inertia weight and the two acceleration coefficients are set to be constants. A set of well-known benchmark optimization problems is then used to investigate the performance of the proposed LHNPSO algorithm and facilitate the comparison with other three types of PSO algorithms. The results show that the easily implemented LHNPSO can converge faster and give a much more accurate final solution for a variety of benchmark test functions.

Performance of PSO, LPSO, LPSO-TVAC and the proposed LHNPSO for solving Ackley function.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 29, April 2015, Pages 386–394
نویسندگان
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