Concurrency and Computation-Practice & Experience最新文献

{"title":"Practical Universal Designated Verifier Transitive Signature Proof Scheme for Graph-Based Data Systems","authors":"Yucan Xu,&nbsp;Qixin Wang,&nbsp;Yufei Ren,&nbsp;Ying Hu,&nbsp;Fei Zhu,&nbsp;Wei Wu,&nbsp;Shaojun Yang","doi":"10.1002/cpe.70295","DOIUrl":"https://doi.org/10.1002/cpe.70295","url":null,"abstract":"<div>\u0000 \u0000 <p>Transitive signatures are a special type of homomorphic signature proposed by Turing Award winners Micali and Rivest, which are highly suitable for authenticating dynamically growing graph-based data systems. In such a signature scheme, anyone with the signer's public key is allowed to generate a signature for a composed edge <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>i</mi>\u0000 <mo>,</mo>\u0000 <mi>k</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(i,kright) $$</annotation>\u0000 </semantics></math>, from two signatures on adjacent edges <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>i</mi>\u0000 <mo>,</mo>\u0000 <mi>j</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(i,jright) $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>j</mi>\u0000 <mo>,</mo>\u0000 <mi>k</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(j,kright) $$</annotation>\u0000 </semantics></math>. To prevent the problem of malicious dissemination of signatures by verifiers leading to data privacy leakage, researchers have proposed a series of universal designated verifier transitive signature (UDVTS) schemes. However, existing work requires that the designated verifier create its own secret-public key pair using the public key parameters provided by the signer. Besides, these schemes suffer from significant performance defects due to expensive pairing or exponentiation operations. In this work, we design a pairing-free and exponentiation-free UDVTS proof scheme based on the SM2 digital signature algorithm and a zero-knowledge proof scheme. We prove the security of our construction based on rigorous cryptographic assumptions. The performance comparison with related work shows that our UDVTS proof scheme has an optimal computational cost and desirable communication cost. For example, compared to the state-of-the-art work, we reduce the signing cost by <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>70</mn>\u0000 <mo>.</mo>\u0000 <mn>54</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 70.54% $$</annotation>\u0000 </semantics></math> and the designated verification cost by <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>96</mn>\u0000 <mo>.</mo>\u0000 <mn>05</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 96.05% $$</annotation>\u0000 </semantics></math>.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing the Fairness Issue of Large Music Models: A Blockchain Approach","authors":"Qinyuan Wang,&nbsp;Wenjian Liu,&nbsp;He Zhang,&nbsp;Guofeng Wang","doi":"10.1002/cpe.70292","DOIUrl":"https://doi.org/10.1002/cpe.70292","url":null,"abstract":"<p>With the rapid development of AI, the integration of intelligent techniques into music has been booming rapidly in recent years. However, there is a rising concern about the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant under-representation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Additionally, we introduce a novel blockchain-based approach to ensure transparency and fairness in the data collection and model training processes. Blockchain technology enables secure, decentralized, and verifiable tracking of dataset contributions, ensuring that diverse cultural elements are adequately represented. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings underscore the significance of cultural diversity in enhancing the user experience and promoting ethical AI practices. The study also discusses challenges, such as dataset limitations, model adaptation complexity, and the integration of blockchain technology. It suggests directions for future research to promote fairness and inclusivity in music AI further.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interest-Guided Adaptive Bidirectional Transfer via Graph Neural Networks for Cross-Domain Recommendation","authors":"Xinyu Zheng,&nbsp;Shuguang Zhang,&nbsp;Yunlong Wang,&nbsp;Yu Cheng,&nbsp;Liangpeng Hu,&nbsp;Jiaxin Yue,&nbsp;Liming Liu","doi":"10.1002/cpe.70293","DOIUrl":"https://doi.org/10.1002/cpe.70293","url":null,"abstract":"<div>\u0000 \u0000 <p>Cross-domain recommendation (CDR) aims to leverage rich data from multiple domains to deliver personalized recommendations. However, existing methods primarily rely on overlapping users to transfer knowledge across domains. This approach overlooks the fact that individuals may exhibit different or even conflicting preferences across domains, making it difficult to effectively address the diversity of users' cross-domain interests. According to the principle of collaborative filtering, a user can share similar preferences with other users, regardless of their domain affiliation. Therefore, cross-domain knowledge transfer should also extend to similar users, necessitating the accurate capture of latent cross-domain user associations. To overcome these limitations, this paper proposes an Interest-Guided Adaptive Bidirectional Transfer via Graph Neural Networks for Cross-Domain Recommendation(IGbtCDR). The method incorporates a bidirectional mapping network module, constructed using Multilayer Perceptrons, to establish a personalized cross-domain transfer matrix between source and target domains. It enables topologically unreachable but distantly similar users to form connections, facilitating the efficient capture and propagation of long-range cross-domain user associations while dynamically adapting to users' evolving cross-domain interests. Furthermore, an interest-guided bidirectional update module, built upon Multi-head Attention mechanisms, is introduced to dynamically mine user relationships. This component overcomes the limitations imposed by original topologies or overlapping users, thereby enhancing personalized recommendation performance. Extensive experiments on four real-world datasets demonstrate that IGbtCDR significantly outperforms state-of-the-art baselines, achieving average relative improvements of 7.14%, 15.14%, 6.57%, and 9.84% in HR@10 and 4.29%, 6.72%, 15.35%, and 13.08% in NDCG@10 across the datasets.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exam Suggestion System Using Beluga Whale Optimization","authors":"Samet Diri,&nbsp;Selçuk Öğütcü,&nbsp;Remzi Gürfidan,&nbsp;Bekir Aksoy","doi":"10.1002/cpe.70251","DOIUrl":"https://doi.org/10.1002/cpe.70251","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, an exam recommendation system was developed using the Beluga Whale Optimization (BWO) algorithm. The system generates balanced exams by selecting the most appropriate questions from a question bank consisting of approximately 12,285 questions according to difficulty, distinctiveness, and frequency of use in previous exams. The performance of BWO was compared with the classical Genetic Algorithm (GA), and it was observed that BWO works much faster and more effectively. For example, in the preparation of a 50-question exam, BWO works in an average of 0.2424 s, while GA completes the same process in 0.5954 s. According to the targeted difficulty criteria, BWO gave results approximately 60 times faster than GA (0.0034 vs. 0.204 s). In addition, the statistical structure of the generated exams showed high agreement with the general characteristics of the question bank. Significance tests (Kolmogorov–Smirnov and Anderson-Darling) supported this agreement. In the results of the survey conducted with academicians, 76.9% positive feedback was received, and the performance of the system was evaluated with a score of 9.31 out of 10.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Modal Vaas Selection in Smart Mobility Networks via Spectral Hyper-Graph Clustering and Quantum-Driven Optimization","authors":"Zaki Brahmi,&nbsp;Haithem Mezni,&nbsp;Hela Elmannai,&nbsp;Reem Alkanhel","doi":"10.1002/cpe.70260","DOIUrl":"https://doi.org/10.1002/cpe.70260","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, smart mobility networks have experienced significant growth due to the integration of key technologies such as cloud computing, edge intelligence, and the Internet of Things (IoT) into transportation infrastructure. When combined with the principles of service-oriented computing (SOC), various transportation modes now feature intelligent capabilities, including eco-driving assistance, emergency service integration, V2X communication, environmental sensors, in-vehicle infotainment, Over-the-Air (OTA) updates, driver behavior monitoring, and AI-powered assistance. This has led to the emergence of Connected Vehicle as a Service (CVaaS) as a new paradigm for smart vehicles and transportation services. However, with the increasing complexity of AI-driven features and integration with smart city infrastructure, traditional recommender systems can no longer meet user requirements such as personalized connectivity preferences and eco-friendly route optimization. CVaaS recommendations also inherit challenges from traditional transportation systems, including multi-modal integration (e.g., coordinating smart buses and autonomous vehicles), environmental considerations (e.g., smart parking and dedicated lanes for autonomous cars), uncertain demand, user trust, regulatory compliance, and data privacy concerns. In this article, we address the challenges of multi-modal transportation and environmental uncertainty, such as traffic congestion and VaaS demand fluctuations. By modeling Smart Urban Network (SUN) traffic and VaaS demand, we predict congestion patterns and VaaS availability using a Long Short-Term Memory (LSTM) model. Additionally, we apply Spectral hyper-graph Theory to cluster the SUN into closely connected regions, identifying traversed areas for trip requests. These preprocessing steps help eliminate high-congestion zones and low-demand VaaS services, improving trip efficiency. Finally, inspired by the combinatorial nature of VaaS selection, we propose a Quantum-Inspired variant of the Gravitational Search Algorithm (Q-GSA) to explore and evaluate possible VaaS combinations, ultimately selecting an optimal set of smart transportation services. Experimental comparisons with four benchmark methods confirm the superiority of our approach in terms of efficiency and solution quality.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Semantic Segmentation of High-Resolution Remote Sensing Data: Utilizing a Novel Enhanced Boundary-Aware Network for Efficient Processing","authors":"","doi":"10.1002/cpe.70273","DOIUrl":"https://doi.org/10.1002/cpe.70273","url":null,"abstract":"<p>\u0000 <span>Jie Shen</span>, <span>Xinran Du</span>, &amp; <span>Houqun Yang</span>. <span>Improved Semantic Segmentation of High-Resolution Remote Sensing Data: Utilizing a Novel Enhanced Boundary-Aware Network for Efficient Processing</span> <i>Concurrency and Computation: Practice and Experience</i>, <span>2025</span>; <span>37</span>:e70177.</p><p>In the Acknowledgments section, the text “This research was funded by Haikou Science and Technology Plan Project (2022-007, 2022-015) and Hainan Province Science and Technology Special Fund (Grant No. SQ2025DXZJGX0035).” was incorrect. This should have read: “This research was funded by Haikou Science and Technology Plan Project (2022-007, 2022-015) and Hainan Province Science and Technology Special Fund (Grant No. ZDYF2025GXJS184).”</p><p>We apologize for this error.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Active Queue Management Algorithm for Packet Management in Software Defined Networking","authors":"Khoshnam Salimi Beni,&nbsp;Mohammadreza Soltanaghaei,&nbsp;Rasool Sadeghi","doi":"10.1002/cpe.70239","DOIUrl":"https://doi.org/10.1002/cpe.70239","url":null,"abstract":"<div>\u0000 \u0000 <p>Bufferbloat is a significant issue in network switches, arising from excessive packet buffering that leads to increased latency and degraded network performance. This happens when switches accumulate too many packets in their buffers, which causes transmission delays and negatively affects network efficiency. To address this problem, active queue management (AQM) algorithms are employed to dynamically adjust queue sizes and prevent congestion by selectively dropping packets. However, determining the optimal buffer size is crucial, as buffers that are too small can result in packet loss and reduced throughput. The integration of software-defined networking (SDN) technology offers a promising solution by enabling efficient network configuration and monitoring. By incorporating AQM algorithms within SDN environments, significant improvements in network performance can be achieved. This paper introduces a novel hybrid active queue management (HAQM) algorithm, which combines elements of both packet-oriented and delay-oriented AQM techniques within an SDN framework. The evaluation demonstrates that the HAQM algorithm effectively enhances network performance by mitigating issues related to packet loss, delay, and jitter, outperforming existing algorithms like CoDel, CoBALT, ARED, and ECN.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of the Full Process of Code Search Based on Deep Learning","authors":"Mengge Fang,&nbsp;Haize Hu,&nbsp;Feiyu Hu,&nbsp;Jianxun Liu","doi":"10.1002/cpe.70277","DOIUrl":"https://doi.org/10.1002/cpe.70277","url":null,"abstract":"<div>\u0000 \u0000 <p>As a pivotal technology for enhancing software development efficiency, research on code search based on deep learning has emerged as a current hotspot. This review systematically deconstructs the entire code search process into four core stages: dataset construction, code preprocessing, heterogeneous representation model construction, and query expansion, while conducting an in-depth analysis of the application status and challenges of deep learning technologies. In dataset construction, the Q and A pairs and C and D pairs relied on by deep learning models suffer from a lack of standardization. For example, CodeSearchNet exhibits insufficient cross-lingual versatility, and CoDesc has incomplete noise filtering. During the code preprocessing stage, bottlenecks such as AST granularity selection and sequence information redundancy restrict the efficiency of feature extraction. Although the introduction of transformer and graph neural networks has optimized structural representation, a unified evaluation mechanism is lacking. In the research of heterogeneous representation models, while LSTM, CNN, and pretrained models (such as CodeBERT) effectively narrow the semantic gap, their cross-domain search accuracy is insufficient. In terms of query expansion, deep learning-based keyword expansion and intent completion methods struggle to capture users' real needs due to low semantic alignment accuracy. This review proposes, for the first time, a standardized dataset construction framework integrating multimodal data, a syntax-semantic dual-layer preprocessing evaluation mechanism, a cross-domain transfer representation model, and a large language model-driven intent dynamic expansion scheme. These contributions lay a theoretical foundation for the systematic development of code search technologies and provide cross-task methodological references for related fields such as code clone detection.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Robot Path Planning Based on Lens Imaging Reverse Learning Harris Hawk Algorithm in Dynamic Environment","authors":"Xinlu Zong,&nbsp;Jiaxin Hao,&nbsp;Fucai Liu","doi":"10.1002/cpe.70266","DOIUrl":"https://doi.org/10.1002/cpe.70266","url":null,"abstract":"<div>\u0000 \u0000 <p>To overcome the difficulties of avoiding obstacles in real time and being vulnerable to local optima in multi-robot path planning (MRPP) in unfamiliar locations, a lens imaging reverse-based learning Harris Hawks optimization algorithm (LRHHO) is proposed. Initially, the Latin hypercube sampling method is employed for population initialization to enhance the diversity and uniformity of the population. Subsequently, during the local exploitation phase, the lens imaging reverse-based learning strategy is introduced to refine individual position updating mechanisms. It is complemented by an adaptive roulette wheel mechanism designed to select between current solutions and their reverse-based counterparts. Finally, a restart mechanism is implemented for inferior individuals to improve the overall evolutionary efficacy of the population. Comprehensive evaluations on benchmark test functions demonstrate the superior optimization performance of LRHHO compared to existing algorithms. A real-time MRPP model is constructed utilizing relative positioning methods, where LRHHO optimizes robots' velocity and angular parameters to determine subsequent positions. The system integrates three coordinated obstacle avoidance mechanisms: static obstacle avoidance, dynamic obstacle avoidance, and inter-robot coordination, enabling rapid responses to randomly moving and unforeseen obstacles. Simulation experiments conducted in two scenarios with varying complexity levels reveal that the proposed method achieves intelligent real-time obstacle avoidance while coordinating concurrent multi-robot operations. Comparative results indicate that LRHHO generates higher-quality paths with enhanced efficiency relative to alternative algorithms.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIFTTA: Multi-Strategy Improved Football Team Training Optimization Algorithm for Engineering Applications","authors":"Zhumei Sun,&nbsp;Jinhua Zhang,&nbsp;Qi Wang,&nbsp;Xinchun Jia,&nbsp;Aoqi Xiao,&nbsp;Zekai Chen","doi":"10.1002/cpe.70282","DOIUrl":"https://doi.org/10.1002/cpe.70282","url":null,"abstract":"<div>\u0000 \u0000 <p>The football team training algorithm (FTTA) is a novel meta-heuristic optimization technique inspired by the three stages of a football team's training process: collective, group, and individual training. Although FTTA exhibits good competitiveness in comparison with other algorithms, it still has a number of drawbacks, including slow convergence speed, low convergence accuracy, insufficient perturbation, and a propensity to enter local optima when solving some complex problems with high dimensional and non-linear constraints. To address these drawbacks, this paper introduces an improved variant of FTTA, termed multi-strategy improved football team training algorithm (MIFTTA). First, an adaptive bilateral factor is introduced to effectively balance the global exploration and local exploitation capabilities of the algorithm. Second, an adaptive oscillating inertia weighting factor is implemented to accelerate the convergence process. Then, building on the adaptive cluster grouping mechanism of the original algorithm, an inter-group communication mechanism is integrated to enhance population diversity during the convergence process, thereby improving the convergence accuracy. Finally, a population bi-directional restart mechanism is devised to strengthen the algorithm's ability to escape from the local optima and explore the solution space more comprehensively. To validate the overall performance of MIFTTA, it is compared with various state-of-the-art algorithms in the CEC2017 and CEC2022 benchmark suites. The results show that MIFTTA achieves average rankings of 1.48 and 2.08 on the two test suites, respectively, with an overall final rank of 1. In the majority of test cases, MIFTTA provides more accurate and reliable solutions than other competitors. Furthermore, MIFTTA is applied to six real-world engineering optimization problems and two photovoltaic model parameter identification problems. The experimental results demonstrate that MIFTTA outperforms the competing algorithms in terms of solution quality and computational efficiency, showing its potential for solving complex optimization problems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}