Partial Order Clustering and Its Hybridization with Intelligent Optimization Algorithm for An Application to Multi-task Assignment

This paper proposes an intelligent optimization clustering strategy based on partial order clustering to address the issue of conditional clustering, presenting a novel higher-order clustering method. Simultaneously, aiming to enhance the efficiency of multi-task assignment, the algorithm is applied to solve large-scale task assignment problems. Initially, a partial order table is established based on the distances between each point's coordinates and all centroids, which utilizes the partial order clustering method to allocate all points to their corresponding clusters while meeting clustering constraints. Subsequently, an improved intelligent algorithm optimizes the centroids of the partial order clustering, reducing the dimensionality of the optimization problem and enabling rapid determination of the optimal solution. Numerical simulation results demonstrate that the proposed algorithm can rapidly solve conditional clustering problems while also efficiently addressing large-scale multi-task assignment problems.