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Multi-objective Optimization Based on Improved Differential Evolution Algorithm

Journal from gdlhub / 2016-11-15 03:08:07
Oleh : Shuqiang Wang, Jianli Ma, Telkomnika
Dibuat : 2014-12-01, dengan 1 file

Keyword : differential evolution, effect of parameters, numerical experim ent, multi-objective optimization
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/531

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions.

Deskripsi Alternatif :

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions.

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