Multimodal sequential recommendation aims to mine the temporal dependencies within user interaction sequences by fusing heterogeneous data such as text and images, so as to accurately model users’ dynamic preferences. However, existing methods remain insufficient in the deep fusion of structured attributes and visual semantics, and are prone to semantic drift and inference hallucination in long-sequence modeling, which limits their ability to cope with information overload in real-world scenarios. To address these issues, this paper proposes a Multimodal Knowledge Graph Retrieval-Augmented Generation framework (MKGRAG) for multimodal sequential recommendation. The framework first builds a multimodal knowledge graph from the textual and visual metadata of items, and then extracts local knowledge subgraphs related to candidate items through a subgraph retrieval module, which are converted into natural language descriptions by a graph-to-text conversion module. On this basis, a multimodal large language model is employed to extract the deep semantics of visual content, so as to construct item summaries that integrate structured knowledge and visual semantics. For user preference modeling, we design an LLM-based recurrent user preference summarizer that captures the temporal dynamics of user preferences by progressively refining user interest representations. In addition, a Low-Rank Adaptation (LoRA) strategy is adopted to parameter-efficiently fine-tune the large language model, reducing the training and deployment costs. Experimental results on three public benchmark datasets—MicroLens, Amazon-Games, and Amazon-Baby—show that MKGRAG improves AUC by 2.39, 1.90, and 3.56 percentage points over the strongest baseline MLLM-MSR, respectively, verifying its effectiveness in dynamic preference modeling and efficient information processing.
The transition to sustainable energy systems has heightened global interest in solar energy as a clean and renewable power source. Central to this technology is the photovoltaic (PV) cell, which directly converts solar radiation into electricity through the photovoltaic effect. Despite the relative simplicity of PV cell structures, accurately modeling their electrical behavior remains a complex task due to the nonlinear, high-dimensional, and multi-modal characteristics of the underlying optimization problems. To address these challenges, this study proposes an Improved Multi-Operator Differential Evolution (IMODE) algorithm tailored to address these challenges. IMODE incorporates multiple mutation strategies within the Differential Evolution framework and employs a self-adaptive mechanism that dynamically assigns strategies to individuals based on their historical success rates. The effectiveness of the proposed IMODE is first validated using the CEC2022 benchmark test suite. Subsequently, its performance is evaluated across six benchmark PV modeling problems, comprising three circuit-based models (single diode, double diode, and triple diode) and three commercial PV modules (Photowatt-PWP201, STP6-120/36, and STM6-40/36). Comparative analysis with seven state-of-the-art DE variants reveals that IMODE achieves superior estimation accuracy with root mean square error of 9.8602187789E–04 for the single-diode model, 9.8248485178E–04 for both the double-diode and triple diode models, 2.4250748680E–03 for Photowatt-PWP201 module, 1.6600603125E-02 for STP6-120/36, and 1.7298137099 STM6-40/36 for the STM6-40/36 module. In addition, IMODE demonstrates faster convergence, achieving optimal solutions with an average runtime of approximately 1 s. Furthermore, statistical validation through the Friedman and Wilcoxon tests highlights the robustness and superior overall performance of IMODE, positioning it as a reliable tool for accurate PV model identification.
The energy-efficient distributed flexible job shop scheduling problem (EE-DFJSP) requires the simultaneous optimization of makespan and total energy consumption. Due to conflicting objectives and the combinatorial complexity of the decision space, existing metaheuristic algorithms often exhibit premature convergence and insufficient adaptability in both local search and energy-saving strategies. To address these challenges, this paper proposes a hybrid memetic algorithm, termed QNiche-MA, which is built upon the integration of adaptive niche selection and Q-learning. A hybrid chaotic–heuristic initialization strategy is first proposed to improve the quality of the initial population. An adaptive niche-based parent selection mechanism is then introduced to preserve diversity and guide the evolutionary process. In addition, an adaptive multi-intensity energy-saving strategy is designed to reduce non-productive energy consumption by dynamically adjusting optimization intensity according to the population state. Furthermore, a Q-learning-based local search is incorporated to select neighborhood operators. Experimental results on benchmark instances show that QNiche-MA achieves competitive performance in terms of HV, IGD, and I_ϵ^+ , demonstrating its effectiveness in solving the EE-DFJSP.
Balancing convergence and diversity remains a key challenge in multi-objective optimization. This paper proposes SOM-MSMOEA, a novel multi-stage evolutionary algorithm that uses a self-organizing map (SOM) to guide the search process. The evolution is divided into three stages: early, middle, and late. In the early stage, a SOM-based uniformity enhancement strategy improves population spread to avoid premature concentration. In the middle stage, an elite-led global search combined with SOM-guided local search in the convergence subspace accelerates front approximation. In the late stage, SOM-driven local search in the diversity subspace refines solution distribution. SOM-MSMOEA exploits the topological structure of SOM to guide stage-specific search strategies, effectively coordinating convergence and diversity throughout the optimization process. Experimental results demonstrate its competitive performance across a wide range of problems.
This work focuses on the insertion and evaluation of a reinforcement learning algorithm based on the Multi-Armed Bandit method within a multi-agent framework for combinatorial optimization. The algorithm was integrated into the cooperation structure of the framework to manage the pool used for sharing solutions among agents and to reduce the occurrence of inefficient steps during the cooperation process, guiding agents toward the selection of better solutions. To enable and support this integration, the framework’s cooperation structure and the diversity of agents were also analyzed. The cooperation structure intermediates the communication among agents, while diversity is ensured by implementing agents with different behaviors in the framework. The experiments were conducted using the Vehicle Routing Problem with Time Windows as the test problem. The results of computational experiments showed that the cooperation structure of the framework and the diversity of agents are of paramount importance, enhancing the quality of the objective function values and runtime achieved so far in tests carried out with the framework. Additionally, the results showed better convergence in the search structure of agents when the Multi-Armed Bandit Pool was incorporated into their cooperation structure, achieving improved runtimes and, in certain cases, enhancing the objective function values as this method prevented agents from exploring inappropriate spaces during the search process.
