An Improved Adaptive Niche Differential Evolution Algorithm
Abstract: Differential
evolution (DE) algorithm is a random search algorithm by referring to the
natural genetic and natural selection mechanism of the biological world and it
is used to process the complicated non-linear problems which are difficult to
be solved by traditional computational methods. However, subject to its own
mechanism and single structure, the basic DE algorithm is easy to get trapped
into local optimum and it is difficult to handle high-dimensional and
complicated optimization problems. In order to enhance the search performance
of the DE algorithm, this paper uses the idea of niche, decomposes them entire
population into several niches according to the fitness, perform population
selection by integrating the optimum reservation strategy to realize the
optimal selection of niche, adjusts the fitness of the individual of the
population, designs the adaptive crossover and mutation operators to make the
crossover and mutation probabilities change with the individual fitness and
enhances the ability of DE algorithm to jump out of the local optimal solution.
The experiment result of benchmark function shows that the method of this paper
can maintain solution diversity, effectively avoid premature convergence and
enhance the global search ability of DE algorithm.
Author: Hui Wang, Changtong
Song
Journal Code: jptkomputergg160308
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGj4FQv1aMKKBVC4_mesGV_ZBAKWTejNaV2HxifdICn1Si6-Cbih_Nn3RHQNCq1oxvhyRv2U9yPX6t4k-PCOSIkqYXB__v7DbFjwnVn73zgsW72l7sqKX5dvQ2XVxnqcLrw2CvPzs63oA/s320/E+JURNAL.gif)