By using a new adaptive mutation operator, this paper proposes a modified adaptive differential evolution (ADE) algorithm to improve the optimum speed and performance of the differential evolution algorithm. The mutation operator is adjusted by the relationship between every individual′s fitness and the best one′s fitness. The value of mutation operator is bigger at the beginning of the evolutionary process and will become smaller as the individual tending the optimal solution so as to quickly and stably approximate the best individual. After every basic mutation, crossover and competition, a new competition with a random swarm is added so as to effectively jump out of the local optimum and enhance the ability of global search. The simulation results for four classic functions show that both the convergence speed and accuracy of ADE are significantly superior to the differential evolution (DE) algorithm and the adaptive differential evolution algorithm that is based on the generation.