Self-adaptive Differential Evolution Algorithm with the New Mutation Strategies

This paper presents a self-adaptive differential evolution algorithm with the new mutation strategies for solving unconstrained global optimization problems. In the new algorithm, we proposes three novel mutation strategies based on the current individual experience and randomly generated individual, scaling factor F and crossover rate CR are adaptive various by using the previous learning experience is utilized to balance the global exploration and local exploitation; the target individuals will be mutation according to the mutation probability, adaptive mutation can enhance the algorithm escape from local optima and avoid premature convergence. Numerical experiments and comparisons on a set of well-known high dimensional benchmark functions indicate that the proposed algorithm outperforms and is superior to the standard DE in terms of the optimal value, convergent speed and accuracy.