Existing AUV path planning methods still face limitations in modeling real three-dimensional (3D) maritime search and rescue (SAR) environments, multi-objective evaluation and safety constraint expression, and maintaining convergence stability in complex search spaces. To address these issues, we propose a dual-population cooperative algorithm that integrates a large language model (LLM) with an evolutionary algorithm (EA) for marine SAR tasks, named LLM_EA. Firstly, we integrate real seabed topography and 3D ocean currents to construct a unified navigation environment. A multi-objective evaluation framework is further designed to balance energy consumption, safety risk, and attitude stability. During the evolutionary process, LLM_EA performs parallel co-evolution between an EA population for numerical search and an LLM population for semantic reconstruction. A feasibility assurance and cross-population information exchange mechanism is designed to improve feasibility and efficiency, and a topological diversity preservation mechanism is introduced to mitigate topological collapse and maintain structural diversity. Comparative experiments show that LLM_EA consistently generates feasible trajectories under diverse geographical environments and complex constraints while achieving better overall performance and more stable convergence than multiple representative algorithms. Ablation experiments further confirm the contributions of the topological diversity preservation mechanism and the LLM strategy. This framework offers a reusable solution for efficient and robust AUV path planning in real-world 3D maritime SAR scenarios.

With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-value information delivery and public opinion monitoring on the internet platforms. The current state-of-the-art models for predicting information popularity utilize deep learning methods such as graph convolutional networks (GCNs) and recurrent neural networks (RNNs) to capture early cascades and temporal features to predict their popularity increments. However, these previous methods mainly focus on the microfeatures of information cascades, neglecting their general macroscopic patterns. Furthermore, they also lack consideration of the impact of information heterogeneity on spread popularity. To overcome these limitations, we propose a physics-informed neural network with adaptive clustering learning mechanism, PIACN, for predicting the popularity of information cascades. Our proposed model not only models the macroscopic patterns of information dissemination through physics-informed approach for the first time but also considers the influence of information heterogeneity through an adaptive clustering learning mechanism. Extensive experimental results on three real-world datasets demonstrate that our model significantly outperforms other state-of-the-art methods in predicting information popularity.

Prediction-based dynamic multi-objective optimization algorithms (DMOEAs) are widely used to explore the relationships of Pareto-optimal solutions (POSs) under continuous time steps, aiming to tackle dynamic multi-objective optimization problems (DMOPs). However, DMOPs with irregular POS shapes pose significant challenges to the quality of predicted solutions owing to the misaligned binding of solutions. To bridge this gap, this paper proposes a learning algorithm based on similarity identification and knowledge transfer, called SIKT-DMOEA, which comprises the following three steps. Firstly, a cluster centers-driven feedforward neural network (CCD-FNN) with global optimal binding assignment is constructed, aiming to learn the regional POS dynamics between adjacent environments. Secondly, a similarity identification technique archives valuable knowledge in historical environments and transfers it to the current environment for evolutionary acceleration. Finally, a population reconstruction strategy is presented for adaptive guidance according to the dominant property of each solution, which approximates the new POS with superior convergence and distribution. Comprehensive experiments demonstrate that SIKT-DMOEA manifests competitiveness when addressing DF test problems and one real-world application problem compared to state-of-the-art DMOEAs. SIKT-DMOEA has corroborated its capability of effectively reducing the loss of population convergence and diversity facing different patterns of environmental changes.

With the rapid development of satellite technology, communication satellites have become an indispensable part of modern infrastructure. They serve as a key pillar for the future integrated communication satellite network (CSN). However, the increasing number of communication satellites presents significant challenges for data transmission between the satellite and ground station. This article focuses on transmitting communication data by scheduling resources for communication tasks. The goal of data transmission scheduling optimization in CSN (DTSOCSN) is to design a scheduling scheme that maximizes task profit across satellite–ground links, considering the constraints of the two working modes of communication satellites. To solve DTSOCSN, a mixed-integer programming model is developed, which incorporates various constraints such as the conditions for feed switching operation and the limitations of task execution windows. Based on the complexity of the problem, we propose an evolutionary algorithm with memory guidance (MGEA). The algorithm takes into account the memory dependence of Caputo fractional-order differential and innovatively designs a crossover operator, called Caputo crossover (CX). This crossover method uses the genetic information stored in memory to guide the crossover operation of the next generation, thereby forming a smooth optimization path and improving the search efficiency of the algorithm. In addition, an elite opposition-based heuristic initialization method and a tracking variation strategy are designed to enhance the algorithm’s ability to find high-quality initial solutions and perform local optimization. Experimental validation proceeds in two stages: first, multiscale simulations demonstrate MGEA’s superior performance over existing mainstream algorithms in task profit, convergence speed, resource utilization, and search efficiency. Second, to verify the contribution of the CX operator, it is integrated into several classical algorithms for comparative testing on benchmark problems. The results consistently show that algorithms using the CX operator achieve significant performance advantages compared with those relying on traditional crossover operators. This study not only provides an effective solution for DTSOCSN but also offers new idea for solving other types of satellite scheduling problems.

Due to the increasing demand for remote sensing imaging products, the agile earth observation satellite scheduling problem (AEOSSP) has garnered significant attention. In response, this paper proposes a reinforcement learning-based dung beetle optimization (RLDBO) algorithm to address AEOSSP. The proposed method dynamically adjusts the proportions of four types of dung beetles (ball-rolling beetle, brood ball beetle, small dung beetle, and thief beetle), enabling adaptive optimization of the scheduling scheme to better handle the complexities and uncertainties of the search space. The Q-learning mechanism guides the adjustment of these proportions, effectively balancing global exploration and local exploitation at different stages of the search process. Experimental results demonstrate that the RLDBO algorithm effectively solves AEOSSP across multiple instances, and it outperforms other algorithms in various aspects, including optimization performance, convergence speed, and scheduling effectiveness. The experimental validation confirms that RLDBO significantly enhances the efficiency and effectiveness of agile earth observation satellite scheduling.

In recent years, prediction-based algorithms have attracted much attention for solving dynamic multiobjective optimization (DMO) problems in the evolutionary computing community. However, this class of algorithms still has potential for further improvements by enhancing the historical information extraction approach to balance convergence and diversity. In this article, we propose a DMO algorithm based on a recurrent neural network (RNN) to balance the population’s convergence and diversity in dynamic environments. The RNN model in the proposed algorithm employs online learning in order to constantly improve according to the increasing evolutionary information. Meanwhile, differing from most existing prediction-based algorithms, the learning machine is not limited by assumptions, such as linear or nonlinear correlation, when it predicts new solutions for future evolutionary environments. Besides, an auxiliary strategy is performed, which adaptively introduces the random or mutated solutions according to the error losses between the prediction solutions and the optimal solutions in the whole optimization process. The experimental results show that the proposed algorithm is more effective for handling DMO problems than several recent algorithms.

Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm’s robustness but also underscores its efficiency and speed in solving the MOAEOSSP.

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the$Q$value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.

The financial security is the lifeblood of a company. Effective identification of corporate financial fraud can protect the safety of funds for investors in some sense. This paper proposed a fraud identification model about corporate financial fraud problem based on principal component analysis (PCA) and BP neural network (BP NN). Compared with other methods, there was a significant improvement in the recognition rate of fraud on financial statements. The experimental results shown that our model is effective, which can accurately identify financial fraud and guarantee the 's financial security.