Entropy Weight TOPSIS-Based Local Load Reallocation Strategy

In complex network systems, load redistribution strategy has become a key means to ensure stable system operation. In practical application, load redistribution strategy needs to comprehensively consider several factors, such as node degree, node residual capacity, and shortest path length. To address this issue, this paper proposes a local load redistribution strategy based on entropy weight TOPSIS method. The entropy weight TOPSIS method combines the advantages of entropy weight method and TOPSIS method, and determines the weights of each index by calculating its entropy value, which can objectively reflect the importance of each index in decision-making and avoid the interference of subjective factors. Meanwhile, the TOPSIS method evaluates the advantages and disadvantages of each node by comparing the distance of each node from the ideal solution and the negative ideal solution, so as to determine the more suitable node as the assignable node. In this paper, the effectiveness of local load redistribution strategy based on entropy weight TOPSIS method is verified on BA scale-free network, WS small-world network, and WS-BA composite network, and the results show that the assignable nodes of the load redistribution strategy based on node’s maximum residual capacity and entropy weight TOPSIS have not only higher values of residual capacity, but also their node degree value and shortest path length value, which indicates that the load reallocation strategy is more integrated in selecting the assignable nodes.