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.The K-means clustering algorithm is affected by the initial cluster center, resulting in a low accuracy of the clustering results. The standard flower pollination algorithm (FPA) has slow convergence and low optimization accuracy in the later stage. Therefore, the FPA is improved, and k-means is optimized accordingly. First, a random reverse learning strategy is used to uniformly distribute the population; second, the dynamic transition probability is used to balance the search mode to improve the overall performance of the algorithm; third, the nonlinear inertia weight parameter is introduced into the global search process to improve the global exploration ability; fourth, the optimal individual improves the diversity of the population while decreasing the probability of the algorithm failing. Six standard test functions are used to test the performance of improved flower pollination algorithm (IFPA), and the results show that IFPA is better than FPA in convergence speed and search optimization accuracy. The experimental comparative analysis of k-means cluster optimization based on improved flower pollination algorithm (IFPA-KM) on the University of California Irvine dataset shows that compared with k-means and FPA-KM, IFPA-KM improves the accuracy of clustering and has better stability.

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To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems.