Abstract: In applications of wireless networks such as routing decisions, cloud computing, data center networking, network selection, and edge computing, multi-attribute decision-making (MADM) algorithms are widely adopted due to their effectiveness in solving multi-objective decision-making problems. However, in modern wireless networks, traditional MADM algorithms fail to adequately meet the demands of scenarios involving rapid, continuous, and large-scale service flows. To address this, this paper proposes two enhanced algorithms: iMADM and variable-weight MADM (vw-MADM). Compared to traditional algorithms, the vw-MADM algorithm is simpler and more efficient. In vw-MADM, when one parameter changes, only the utility of that specific parameter needs to be recalculated, while the utilities of other candidate parameters remain unaffected. Its innovation lies in improving accuracy while reducing computational complexity. Additionally, this paper evaluates the properties of the proposed vw-MADM and iMADM algorithms, including rationality, effectiveness, computational complexity, and thresholds for parameter and utility variations. Simulation results demonstrate that the proposed vw-MADM algorithm outperforms traditional MADM and iMADM algorithms in terms of accuracy, computational complexity, and rationality, proving its capability to significantly enhance MADM performance.

Key words: multi-attribute decision-making (MADM), computational complexity, wireless network, variable-weight