Research On The Improvement Of Differential Evolution Algorithm And Its Application

Differential Evolution(DE)algorithm is a kind of parallel stochastic heuristic search method based on floating point encoding.Due to its simple implementation,easy understanding,little control parameters,better convergence performance and strong robustness,it has been successfully applied in science and engineering fields.However,with the increasing complexity of the problem to be solved,DE algorithm also has some drawbacks,such as premature convergence,low convergence precision and slow convergence rate in the later stage of evolution.Therefore,it is of great theoretical research significance and practical application value in improving the convergence performance of DE algorithm and extending the practical application fields of DE algorithm.To deal with the poor performance of DE in solving complex function,a novel hybrid differential evolution algorithm(DEGSA-SL)is proposed by combining the advantages of gravitational search algorithm(GSA).In the proposed algorithm,a new operator called threshold statistical learning is designed.By introducing the operator,the better strategy of DE and GSA can be selected adaptively by learning from the previous success ratio of the two strategies to produce next generations at each iteration in the evolution process,which takes full use of the potential of DE and GSA,ensures the balance between global exploration and local exploitation abilities in the solution spaces,and improves the global search capabilities of the standard DE algorithm.Several complex benchmark functions are employed to test the performance of the DEGSA-SL.The results show that the proposed algorithm not only achieves better convergence precision,robustness and convergence rate,but also avoids the premature convergence problem effectively.Aim at the problem of community detection in complex networks,a novel immune discrete differential evolution(IDDE)is proposed in the framework of standard differential evolution.In the proposed method,initial population is generated in a label propagation way,and discrete differential evolution strategy is utilized for ensuring the global search ability of the IDDE;meanwhile,high-frequency clonal selection mutation operation is applied to the elitist of the population to improve the local exploitation ability and search ability of the IDDE.Computer-generated network and several real-world networks are employed to test the performance of the IDDE.The experiment results show that the proposed algorithm IDDE achieves better search ability and stronger robustness,and it can detect community structure in complex networks effectively.In this paper,a novel hybrid Differential Evolution algorithm is proposed with the analysis of advantages and disadvantages of Differential Evolution,combining with the advantages of other intelligent algorithms.Meanwhile,taking the complex networks as a research object,an improved Differential Evolution algorithm is applied to the detection of the community structure in complex networks with better community division.Consequently,these results are referentially and influentially important in promoting the development of the theory and applying of Differential Evolution.