Recently

inspired by the migratory behavior of monarch butterflies in nature

a swarm intelligence optimization algorithm

called monarch butterfly optimization algorithm (MBO)

is proposed.Since MBO is proposed

it has good performances in various real-world optimization problems.However

migration operator of MBO selects randomly two individuals to generate new offspring

in which the useful search experience of global optimal individual is easily lost.Based on the intrinsic mechanism of the search process of MBO and the character of differential mutation operator

MBO is combined with 7 kinds of DE mutation strategies

respectively.Then a series of experiments are conducted to verify their performance.A DEMBO based on MBO and better differential evolution mutation strategy is presented

in which migration operator is replaced by differential mutation operator to enhance its global optimization ability.The over-all performance of DEMBO is verified and analyzed by 30 typical discounted {0-1} knapsack problem (D {0-1} KP) instances.The experimental results demonstrate that DEMBO can significantly improve the solution quality and convergence speed under the condition of not increasing the time complexity.Meanwhile

the approximation ratio of all the D {0-1} KP instances obtained by DEMBO is close to 1.0.