<p>The current network security situation is becoming increasingly complex and dynamic, and intrusion detection systems are facing severe challenges in terms of performance optimization and detection efficiency. This paper proposes a multi-strategy improved weighted mean of vectors algorithm (MRINFO) to optimize the weights and biases of the Extreme Learning Machine (ELM) classifier in intrusion detection systems, effectively enhancing the classification performance of the system. To address issues such as low solution accuracy and slow convergence of the basic INFO algorithm during execution, MRINFO algorithm proposes and integrates two improvement mechanisms: one is the multi-strategy dynamic stochastic learning pool, which designs and introduces various oppositely learned variants into the learning pool and dynamically updates them to make the candidate solutions more diversified; and the other one is the reinforcement strategy of partial dimension mutation based on feedback priority, which implements scoring operations on each individual dimension and guides the population to converge towards the global optimum. Experimental results show that the MRINFO algorithm performs excellently in the CEC2022 test suite, outperforming other comparative algorithms in terms of optimization accuracy, convergence speed, and stability. In the classification tasks of intrusion detection systems, the ELM classifier optimized by MRINFO shows outstanding performance across various metrics in both binary and multi-class classification tests. This validates the feasibility and effectiveness of the MRINFO algorithm in intrusion detection systems and demonstrates its broad application prospects.</p>