Differential Evolution Algorithms Based on Improved Population Diversity
Zongcheng Liu · Electronics Optics & Control · 2012
Aiming at the premature convergence problem at evolutionary anaphase of Differential Evolution(DE) algorithms,a modified DE algorithm(called DIDE) was proposed,which used diversity theory approach to improve the population diversity.The relationship between changes of population diversity and performance of DE was proved mathematically.A random mutation method was proposed according to the relationship,which could make the algorithms keep the diversity much better and could enhance its global searching ability.The performance of DIDE was evaluated on a test bed of two functions.The numerical results were compared with that of the original differential evolution method,which indicated that this modification enables the algorithm to get a better transaction between the convergence rate and robustness.