Advanced Intelligent Systems Research Articles

A Framework for Promoting IoT- and Digital Twin-Enabled Intelligent Traffic Light Systems in China: An Empirical Study Based on a Malaysian Smart City Facilities Management Case

This study explores the potential for the implementation of an advanced Intelligent Traffic Light System (ITLS) in Chinese urban landscapes, integrating Internet of Things (IoT) and digital twin technologies for sustainable urban development. Using empirical data from the Malaysia Smart Traffic Light Management (MSTLM) program, we assessed the effectiveness of the system on multiple dimensions critical to sustainability. Statistical analysis of eight cities showed significant improvements in vehicle safety programs: average travel time was reduced by 37.7%, congestion index by 38.5%, fuel consumption by 27.3%, and CO2 emissions by 18.4%. In addition, system reliability was significantly improved with 98.3% uptime and 280.5% increase in signal conditioning frequency. The study presents a comprehensive framework tailored to China's urban environment, emphasizing technical architectures, deployment strategies, and policy recommendations aligned with the SDGs. Our findings contribute to the debate on smart city infrastructure management, providing actionable insights for policymakers, urban planners, and stakeholders committed to resilient and sustainable urban growth providing an adaptable roadmap for large-scale deployment in rapidly urbanizing Chinese cities. Our findings bridge Malaysian empirical insights to China’s distinct infrastructure and governance structures.

P-408. Utility of a Large Language Model for Identifying Central Line-Associated Bloodstream Infections (CLABSI) Using Real Clinical Data at Stanford Health Care

Abstract Background Central line-associated bloodstream infections (CLABSI) surveillance can be subjective and time-consuming. Large language models (LLMs) are advanced artificial intelligence systems with potential to assist healthcare professionals in classification tasks. Stanford Health Care recently implemented one of the first secure LLMs, powered by OpenAI’s GPT 4.0, cleared for sensitive health data. We assessed its performance in classifying CLABSI cases.Figure 1:Confusion Matrix of LLM Performance in CLABSI Classification. Methods We selected 40 patients flagged by our surveillance system for CLABSI review from November 2023–March 2024: 20 CLABSIs, consecutively identified, and 20 not-CLABSIs (randomly sampled). We prompted the LLM to determine if patients met the NHSN definition for CLABSI and provided the blood culture results that triggered the alert and the last 2 progress notes from the primary care team at the infection window end (within 3 days after the first positive test). We compared the secure LLM's determinations with those of infection preventionists.Table 1.Cases in which the LLM did not agree with IP assessment for CLABSI.*Community-onset: Blood cultures obtained within 2 days of admission.+NHSN guidelines list Fusobacterium nucleatum as an MBI organism (https://www.cdc.gov/nhsn/pdfs/pscmanual/17pscnosinfdef_current.pdf)Abbreviations: BSI, bloodstream infection; CLABSI, central line-associated infection; CoNS, coagulase-negative Staphylococci; ESBL, extended-spectrum beta lactamase; HIDA scan, IP, infection preventionist, LLM, large language model; MBI, mucosal barrier injury; MSSA, methicillin-susceptible Staphylococcus aureus; NHSN, National Healthcare Safety Network. Results Across 20 CLABSI-positive and 20 CLABSI-negative cases reviewed, the LLM accurately identified 16 of 20 CLABSIs and 7 of 20 not CLABSIs. The sensitivity was 80% (95% CI 57.6%–92.9%), specificity was 35% (95% CI 33.3%–86.5%), and the agreement rate was 57.5% (95% CI 41.2%–73.3%). Among 17 discordant cases, 11 involved clinical data available in the chart but unavailable to the LLM—admission information (4 false-positives), matching organisms (4 false-positives), and central line or symptom status (2 false-negatives, 1 false-positive). If this information was available to the LLM, we expect an adjusted sensitivity of 90% (18/20) and adjusted specificity of 80% (16/20). The remaining discordant cases involved misclassifications of organisms and incorrect identification of infection sources by the LLM. The mean review time by infection preventionists was 75 minutes (SD 48.7 minutes) compared to 5 minutes using the LLM. Conclusion An LLM not specifically trained for CLABSI classification showed high sensitivity using limited patient data. LLM case review required 5 minutes, versus 1 hour for traditional review. These results suggest LLMs could serve as a "first-pass" screening tool for CLABSI detection, helping infection preventionists narrow records needing human review. Disclosures All Authors: No reported disclosures

Development and implementation of a deep learning-based smart evaluation system for animated films

Animation has a rich history of over a century, and scholarly interest in the critique of animated films has evolved alongside its development. With the advent of the Internet and advancements in computer technology, traditional methods of animated movie appreciation are increasingly challenged by new complexities. This paper addresses these challenges by presenting an advanced intelligent system for evaluating animated films, utilizing cutting-edge deep learning models. Our approach begins with a pre-trained deep learning model to classify image categories and extract scene characteristics, while a primary color extraction algorithm captures key color features. Building on this, we propose an innovative deep convolutional neural network (CNN) augmented with fully connected layers to integrate multimodal features, enhancing the accuracy of film evaluations. Furthermore, we introduce a novel deep learning framework designed to detect pixel regions in images that evoke emotional responses. By employing visualization techniques within the convolutional network, we identify emotionally significant regions and enhance these features during initial training, improving the system’s emotional sensitivity. Experimental results demonstrate that our system not only improves the accuracy and efficiency of animated film evaluations but also surpasses existing methods, offering a more nuanced, emotion-aware approach to animation critique. This work advances the field by providing a robust, automated tool for evaluating animated films, offering deeper insights into their emotional and visual components, and contributing to the ongoing evolution of AI-driven film appreciation systems.

Recent Progress in Self-Healing Triboelectric Nanogenerators for Artificial Skins.

Self-healing triboelectric nanogenerators (TENGs), which incorporate self-healing materials capable of recovering their structural and functional properties after damage, are transforming the field of artificial skin by effectively addressing challenges associated with mechanical damage and functional degradation. This review explores the latest advancements in self-healing TENGs, emphasizing material innovations, structural designs, and practical applications. Key materials include dynamic covalent polymers, supramolecular elastomers, and ion-conductive hydrogels, which provide rapid damage recovery, superior mechanical strength, and stable electrical performance. Innovative structural configurations, such as layered and encapsulated designs, optimize triboelectric efficiency and enhance environmental adaptability. Applications span healthcare, human-machine interfaces, and wearable electronics, demonstrating the immense potential for tactile sensing and energy harvesting. Despite significant progress, challenges remain in scalability, long-term durability, and multifunctional integration. Future research should focus on advanced material development, scalable fabrication, and intelligent system integration to unlock the full potential of self-healing TENGs. This review provides a comprehensive overview of current achievements and future directions, underscoring the pivotal role of self-healing TENGs in artificial skin technology.

Advanced Intelligent Systems: Shaping the Future of Robotics and AI

Advanced Intelligent Systems: Shaping the Future of Robotics and AI

Inteligencia artificial en la educación universitaria: Revisión bibliométrica en Scopus y Web of Science

Introduction. Artificial intelligence (AI) today plays an important role in university education; in that sense, the objective of this research is to analyze the scientific production on AI in university education under a bibliometric review in Scopus and Web of Science (WoS). Method. A bibliometric analysis was carried out, where 895 documents published in journals indexed in the Scopus and WoS databases were analyzed. The terms ‘artificial intelligence’, ‘computational intelligence’, ‘AI’, ‘university education’ and ‘university’ were applied as terms, and the Boolean operators AND and OR were used for both databases. Results. We found 848 documents in Scopus and 48 in WoS. China is the country with the highest scientific production. Brain-Broad Research in Artificial Intelligence, Neuroscience, and Education Science are the journals with the highest number of publications in WoS, while in Scopus they are Journal of Physics Conference and Advances in Intelligent Systems and Computing. Regarding the thematic area, it was found that the highest production corresponds to computer science. In terms of affiliation, the authors belong to institutions in China, and the article with the highest number of citations is “Neuropsychological Bases of Self-Improvement of Own Physical Health of Future Teachers in the Course of University Education”. Conclusions. It is necessary to continue research on AI and its implications in university education due to its relevance in the teaching-learning processes and the interdisciplinary innovation that provides tools to reinforce knowledge.

Smart Materials in Green Architecture: The Role of ETFE and Phase Change Materials in Sustainable Building Design

Given the importance of energy in achieving environmental sustainability in green buildings, one of the most significant solutions is to reduce physical building consumption and instead use renewable energy to prevent the depletion of natural resources and environmental pollution. Utilizing advanced technologies and intelligent systems in architecture is one of the methods that can achieve energy management in buildings and ensure comfortable conditions for the occupants of green buildings. In the field of future-oriented architecture, it is expected that in the future, materials and technologies developed over the past century will define the different challenges ahead to elevate sustainable development in the building industry. Using solar energy, one of the latest innovations in buildings, and other measures such as ETFE panels (a green roof idea with an intelligent system) and thermochromic materials, which change color to minimize heat exchange between interior and exterior spaces of the building, show that today's world faces energy crises. The use of smart materials tailored to environmental conditions can significantly impact building design and have beneficial effects in terms of compatibility with the environment, increased lifespan of materials, and the adaptability of materials to changing weather conditions, thus aiding in achieving sustainable architecture. Therefore, this article aims to introduce intelligent materials and their applications and benefits in green building architecture based on library studies. The primary goal is to identify and highlight the most crucial aspects of using intelligent materials and their performance in buildings and how these materials behave and perform in architectural designs. The best way to leverage the benefits of these smart energy-saving materials and management for achieving sustainable architecture will be discussed.

An empirical study of factors influencing usage intention for generative artificial intelligence products: A case study of China

The rapid advancement of generative artificial intelligence technologies has sparked widespread interest, yet understanding of user adoption patterns for these tools remains limited. While the unified theory of acceptance and use of technology model has been widely applied to various technologies, its applicability to generative artificial intelligence products, which present unique challenges and opportunities, has not been thoroughly explored. This gap in knowledge hinders the development of effective strategies for promoting user acceptance and optimising the design of generative artificial intelligence tools. This study extends the unified theory of acceptance and use of technology model to investigate the factors influencing usage intention for generative artificial intelligence products. The study incorporates additional variables such as digital literacy, perceived risk and perceived trust to provide a more comprehensive framework. Using structural equation modelling to analyse survey data, the study found that effort expectancy, performance expectancy, social influence, and perceived trust significantly and positively impact usage intention. In addition, digital literacy indirectly enhances usage intention through effort expectancy, while perceived risk negatively influences usage intention through reduced trust. Notably, facilitating conditions did not exhibit a significant effect on usage intention. These findings offer valuable insights for developers and researchers in the field of generative artificial intelligence, highlighting the importance of user-friendly design, performance optimisation, and trust-building measures. By identifying key factors that drive user adoption, this study contributes to a more nuanced understanding of technology acceptance in the context of advanced artificial intelligence systems, paving the way for more effective development and implementation strategies in this rapidly evolving field.

Advancing Artificial Intelligence: The Potential of Brain-Inspired Architectures and Neuromorphic Computing for Adaptive, Efficient Systems

Brain-inspired AI architecture, also known as neuromorphic computing, seeks to emulate the structure and functionality of the human brain to create more efficient, adaptive, and intelligent systems. Unlike traditional AI models that rely on conventional computing frameworks, brain-inspired architectures leverage neural networks and synapse-like connections to perform computations more similarly to biological brains. This approach offers significant advantages, including lower power consumption, improved learning capabilities, and enhanced problem-solving efficiency, particularly in tasks that require complex pattern recognition and cognitive processes. This paper explores the key components of brain-inspired AI architectures, such as spiking neural networks (SNNs) and neuromorphic hardware, and reviews the latest advancements in this field. We examine their applications across diverse domains, including robotics, autonomous systems, and medical diagnostics, where brain-like adaptability and real-time learning are critical. Additionally, we analyze the challenges associated with scaling these architectures, including hardware constraints and the complexity of accurately mimicking human brain functionality. The potential for combining brain-inspired AI with current machine learning models is also discussed, highlighting future directions for achieving more advanced, efficient, and human-like artificial intelligence systems. This research contributes to the growing body of knowledge on neuromorphic computing and its promise in shaping the future of AI technologies.Moreover, brain-inspired AI architectures have the potential to surpass traditional AI systems in terms of real-time decision-making and learning efficiency, particularly in environments that require adaptive behavior. The use of spiking neural networks (SNNs) in these architectures allows for more biologically plausible models of neuron activity, which can lead to advancements in sensory processing and autonomous decision-making. As neuromorphic hardware continues to evolve, integrating it with existing AI frameworks could enhance both performance and scalability. However, replicating the brain's full complexity remains a significant challenge, particularly in terms of creating energy-efficient hardware capable of supporting large-scale neural networks. Despite these challenges, the development of brain-inspired AI promises to bridge the gap between artificial and human intelligence, offering transformative possibilities for various industries.

Improving Workplace Safety and Health Through a Rapid Ergonomic Risk Assessment Methodology Enhanced by an Artificial Intelligence System

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort.

Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing.

The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.

Wafer-scale integration of two-dimensional perovskite oxides towards motion recognition

Two-dimensional semiconductors have shown great potential for the development of advanced intelligent optoelectronic systems. Among them, two-dimensional perovskite oxides with compelling optoelectronic performance have been thriving in high-performance photodetection. However, harsh synthesis and defect chemistry severely limit their overall performance and further large-scale heterogeneous integration. Here, we report the wafer-scale integration of highly oriented nanosheets by introducing a charge-assisted oriented assembly film-formation process and confirm its universality and scalability. The shallow-trap dominance induced by structural optimization endows the device with a distinguished performance balance, including high photosensitivity close to that of single nanosheet units and fast response speed. An integrated ultra-flexible 256-pixel device demonstrates the versatility of material-to-substrate integration and conformal imaging functionality. Moreover, the device achieves efficient recognition of multidirectional motion trajectories with an accuracy of over 99.8%. Our work provides prescient insights into the large-area fabrication and utilization of 2D perovskite oxides in advanced optoelectronics.

Hafnia-based neuromorphic devices

The excellent complementary metal-oxide-semiconductor compatibility and rich physicochemical properties of hafnia-based materials, in particular the unique ferroelectricity that surpasses of conventional ferroelectrics, make hafnia-based devices promising candidates for industrial applications. This Perspective examines the fundamental properties of hafnia-based materials relevant to neuromorphic devices, including their dielectric, ferroelectric, antiferroelectric properties, and the associated ultra-high oxygen-ion conductivity. It also reviews neuromorphic devices developed leveraging these properties, such as resistive random-access memories, ferroelectric random-access memories, ferroelectric tunnel junctions, and (anti)ferroelectric field-effect transistors. We also discuss the potential of these devices for mimicking synaptic and neuronal functions and address the challenges and future research directions. Hafnia-based neuromorphic devices promise breakthrough performance improvements through material optimization, such as crystallization engineering and innovative device configuration designs, paving the way for advanced artificial intelligence systems.

Integrating Retrieval-Augmented Generation with Large Language Model Mistral 7b for Indonesian Medical Herb

Large Language Models (LLMs) are advanced artificial intelligence systems that use deep learning, particularly transformer architectures, to process and generate text. One such model, Mistral 7b, featuring 7 billion parameters, is optimized for high performance and efficiency in natural language processing tasks. It outperforms similar models, such as LLaMa2 7b and LLaMa 1, across various benchmarks, especially in reasoning, mathematics, and coding. LLMs have also demonstrated significant advancements in addressing medical queries. This research leverages Indonesia’s rich biodiversity, which includes approximately 9,600 medicinal plant species out of the 30,000 known species. The study is motivated by the observation that LLMs, like ChatGPT and Gemini, often rely on internet data of uncertain validity and frequently provide generic answers without mentioning specific herbal plants found in Indonesia. To address this, the dataset for pre-training the model is derived from academic journals focusing on Indonesian medicinal herbal plants. The research process involves collecting these journals, preprocessing them using Langchain, embedding models with sentence transformers, and employing Faiss CPU for efficient searching and similarity matching. Subsequently, the Retrieval-Augmented Generation (RAG) process is applied to Mistral 7b, allowing it to provide accurate, dataset-driven responses to user queries. The model's performance is evaluated using both human evaluation and ROUGE metrics, which assess recall, precision, F1 measure, and METEOR scores. The results show that the RAG Mistral 7b model achieved a METEOR score of 0.22%, outperforming the LLaMa2 7b model, which scored 0.14%.

Intelligent systems and consumer neuroscience in the age of computational advertising

Abstract The main goal of this article is to provide an overview of the use and characteristics of intelligent systems and neuroscience tools applicable in the field of contemporary advertising. The newly emerging field of computational advertising is undergoing dynamic development, and this concept is now placed in the context of advanced intelligent systems, artificial intelligence, and virtual reality. According to the specified parameters, a systematic literature search of scientific publications was carried out and subsequently evaluated. The research questions are focused on the identification of intelligent systems and current consumer neuroscience tools finding application in the current trend of computational advertising. It follows from the processed systematic literature review that there are currently a number of intelligent systems and also a number of tools in the field of consumer neuroscience that can find application within the broader concept of computational advertising. These more or less intelligent systems and neuroscientific tools are already affecting all phases of the advertising life cycle. At the same time, a number of ethical issues associated with the use of both these technologies and tools have been found, which still need to be explored. The article attempts to fill the gap in the lack of literature dealing with this issue. Last but not least, the article contains a critical view of these new technological possibilities and also describes a number of new ethical challenges arising in this area.

CODEX

Project Codex is an advanced artificial intelligence (AI) system designed to provide human-like conversational capabilities. Built on the GPT-3.5 architecture, Codex is a revolutionary project developed by OpenAI. It combines state-of-the-art language models and deep learning techniques to generate responses and engage in meaningful conversations with users. Codex is an innovative project that pushes the boundaries of conversational artificial intelligence (AI) through the development of a sophisticated chatbot built on the GPT (Generative Pre-trained Transformer) framework. Inspired by the remarkable advancements in natural language processing, Codex aims to create an AI system capable of engaging in human-like conversations, providing users with a seamless and intelligent chat experience.

Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement?

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal reasoning. There are also substantial individual differences in LLM capacities. Given the consistent observation of a positive manifold and general intelligence factor in human samples, along with group-level factors (e.g., crystallised intelligence), we hypothesized that LLM test scores may also exhibit positive inter-correlations, which could potentially give rise to an artificial general ability (AGA) factor and one or more group-level factors. Based on a sample of 591 LLMs and scores from 12 tests aligned with fluid reasoning (Gf), domain-specific knowledge (Gkn), reading/writing (Grw), and quantitative knowledge (Gq), we found strong empirical evidence for a positive manifold and a general factor of ability. Additionally, we identified a combined Gkn/Grw group-level factor. Finally, the number of LLM parameters correlated positively with both general factor of ability and Gkn/Grw factor scores, although the effects showed diminishing returns. We interpreted our results to suggest that LLMs, like human cognitive abilities, may share a common underlying efficiency in processing information and solving problems, though whether LLMs manifest primarily achievement/expertise rather than intelligence remains to be determined. Finally, while models with greater numbers of parameters exhibit greater general cognitive-like abilities, akin to the connection between greater neuronal density and human general intelligence, other characteristics must also be involved.

Improved Multi-Objective Beluga Whale Optimization Algorithm for Truck Scheduling in Open-Pit Mines

Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual scheduling used for mining transportation often prioritizes output over efficiency and quality, resulting in high operational expenses, traffic jams, and long lines. In this study, a novel scheduling model with multi-objective optimization was created to overcome these problems. Production, demand, ore grade, and vehicle count were the model’s constraints. The optimization goals were to minimize the shipping cost, total waiting time, and ore grade deviation. An enhanced multi-objective beluga whale optimization (IMOBWO) algorithm was implemented in the model. The algorithm’s superior performance was demonstrated in ten test functions, as well as the IEEE 30-bus system. It was enhanced by optimizing the population initialization, improving the adaptive factor, and adding dynamic domain perturbation. The case analysis showed that, in comparison to the other three conventional multi-objective algorithms, IMOBWO reduced the shipping cost from 7.65 to 0.84%, the total waiting time from 35.7 to 7.54%, and the ore grade deviation from 14.8 to 3.73%. The implementation of this algorithm for truck scheduling in open-pit mines increased operational efficiency, decreased operating costs, and advanced intelligent mine construction and transportation systems. These factors play a significant role in the safety, profitability, and sustainability of open-pit mines.

Roadmap of Adversarial Machine Learning in Internet of Things-Enabled Security Systems.

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model's functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices' malfunction, services' interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement.

Parallel dual adaptive genetic algorithm: A method for satellite constellation task assignment in time-sensitive target tracking

The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm’s sensitivity to parameters, while the parallel mechanism increases the evolutionary process’s computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation.