To address the issues of low convergence accuracy and susceptibility to local optima in the PKO algorithm, this paper proposes a multi-strategy improved IPKO algorithm. First, Latin hypercube sampling is used to avoid overconcentration or neglect of potentially beneficial areas in high-dimensional problems, thus reducing the risk of local optima. Secondly, the positioning fishing mechanism from the OOA algorithm is introduced to enhance exploration of the optimal region and improve the ability to escape from local optima. Finally, a new falling mechanism is integrated to improve search stability and prevent premature convergence. An adaptive mutation rate termination condition is also applied to dynamically balance global exploration and local exploitation, optimizing solution quality and search efficiency. The training-testing accuracy and runtime under different feature dimensions are compared, and the impact of population size and iteration count on the algorithm′s performance is analyzed. Experimental results on 12 benchmark test functions show that IPKO outperforms other comparison algorithms in terms of convergence speed, solution accuracy, stability, and the Friedman test. When applied to the microgrid scheduling problem, IPKO demonstrates lower costs compared to other algorithms, with a reduction of 1.92% over the original PKO, confirming its effectiveness and reliability in practical applications.