An Improved Dynamical Evolutionary Algorithm
Hao Hu · Jisuanji fangzhen · 2009
The dynamical evolutionary algorithm(DEA) is a novel evolutionary computation technology,based on the theory of statistical mechanics.DEA effectively maintains population diversity by driving all individuals to move and to evolve.However,DEA converges slowly and often unexpectedly inclines to converge at local optima in hard function optimization.In this paper,an improved dynamical evolutionary algorithm(IDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal solutions.In order to confirm the effectiveness of this algorithm,IDEA is applied to solve the typical numerical function minimization problems.The experimental results show that IDEA outperforms the DEA in the aspect of convergence velocity and precision.