The progress of scientific development requires the use of high-performance computers for large-scale simulations, resulting in a significant communication overhead and thereby constraining the computer's performance. To address this issue, optimizing the topological mapping of application processes to computing nodes is essential for enhancing the communication performance of high-performance computers. However, this topic has not been extensively explored in the literature. In order to reduce the communication overhead of high-performance applications, this study formulates the optimization of topological mapping from application processes to computing nodes as a quadratic allocation problem. The proposed method collects communication features to assess the communication intimacy between processes and considers the communication relationship between application processes and network topology. To overcome the limitations of traditional genetic algorithms, this study introduces elite learning and adaptive selection into the mutation operator. In this algorithm, individuals undergoing mutation learn from fragments of the best individuals in the current population. Additionally, three functions are selected to control the probability of selecting the elite learning mutation during the mutation process, thereby enhancing the algorithm's efficiency and accuracy. The results of the experiments demonstrate that the suggested methodology yields a noteworthy enhancement in communication performance compared to the widely adopted round-robin approach in NPB test suites. Furthermore, the enhanced genetic algorithm displays superior optimization efficiency in comparison to conventional genetic algorithms and other heuristic approaches.

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