Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

Different from an ordinary project, a large group mission like the unmanned aerial vehicle (UAV) swarm cooperative strike mission is performed by multiple executors and needs to be strictly carried out according to the plan. Because of the complex cooperative relationships between the sub-missions that make up a large mission, a small disturbance may cause a delay in the entire plan. Therefore, the mission process must be precisely controlled in real time to resist disturbances and ensure that the mission proceeds as planned. To address the real-time process control problem of large group missions, we propose a novel dual-code group network plan diagram model that enables plan description and process tracking for complex group missions. Additionally, a mission process closed-loop feedback control system is designed that models the mission process control problem as a mapping from the mission state observation to plan adjustment. Furthermore, an analytic-based mission process control strategy is proposed and rigorously proven to converge and be effective, as well as demonstrate the maximum anti-disturbance capability. Finally, the control strategy is tested on a UAV swarm cooperative strike mission containing 56 sub-missions. The simulation results demonstrate that the proposed control strategy is capable of achieving high, fast, and accurate control for the mission process and enhancing the anti-disturbance capability of the plan by adjusting the mission plan in real time. This will provide a valuable reference for the management of large group missions.

Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper’s capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM’s capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data.

Addressing the real-time control challenges within large-scale, complex resource-constrained project scheduling, this paper investigates control strategies to ensure the on-time initiation of critical task nodes during the execution of aviation cluster mission plans in the presence of disturbances. Conventional resource-constrained project scheduling problem (RCPSP) models typically treat task start times as the primary decision variables, overlooking the intrinsic link between task duration and resource allocation. Moreover, their reliance on intelligent optimization algorithms struggles to simultaneously balance solution accuracy and computational efficiency, thus failing to meet the demands of precise, real-time control. This paper proposes a real-time project schedule control system with the primary objective of preventing delays in critical tasks. The system aims to maximize the remaining anti-disturbance capacity under resource constraints, and establishes five control constraints tailored to the practical problem’s characteristics. The limitations of traditional approaches mainly lie in the fact that they take the start time of each task as the decision variable. When the scale of task quantity in the project is large, the decision dimension increases exponentially; meanwhile, the start times of various tasks are interdependent, leading to extremely complex constraint relationships. To overcome the limitations of traditional methods, this paper introduces a precise control method based on the Critical Path Transform Tree (CPTT). This method takes task duration as the decision variable, calculates the start time of each task using a recursive formula, and integrates expert heuristic knowledge to transform the dynamic network schedule from a “black box” to a “gray box” model. It effectively addresses the technical challenge of reverse mapping in the recursive formula, ultimately realizing precise and real-time control of the project schedule. The simulation results show that while maintaining high solution accuracy, the computational efficiency of the proposed control method is significantly improved to 1.6 s—compared with an average of 6.9 s for the adaptive differential evolution algorithm—thus verifying its effectiveness and practicality in real-time control applications.