کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495799 862839 2013 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Particle swarm optimization with grey evolutionary analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Particle swarm optimization with grey evolutionary analysis
چکیده انگلیسی


• Propose a modified grey relational analysis for PSO algorithm.
• Propose grey evolutionary analysis to evaluate the evolutionary state of a swarm.
• GEA-based algorithm parameters could adapt to the evolutionary state.
• GEA-based PSO can perform a global search with faster convergence speed.

Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions.

Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 10, October 2013, Pages 4047–4062
نویسندگان
, , ,