Abstract：A new differential evolution algorithm based on complex network is presented. Individuals are represented by nodes and dynamic propagation direction is represented by directed edges, thereby constructing a complex network. in particular,in the mutation stage, the mechanism of selecting the target vector based on probability using the individual objective function value and network parameter information is proposed, and the convergence factor is introduced to change the convergence speed of different function types. In the selection phase, a new sorting-based selection strategy is proposed. Finally, the proposed algorithm is tested with 21 standard test functions, and compared with some mainstream differential evolution algorithms. The test results show that the proposed algorithm has significant advantages both in convergence speed and in solution accuracy.
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