Concurrency and Computation-Practice & Experience最新文献

{"title":"Bridging Functional Encryption and Secure Function Evaluation: Toward Efficient and Coercion-Resistant Multi-Party Systems","authors":"Rahat Naz,&nbsp;Jaydeep Howlader,&nbsp;Shahnawaz Ahmad","doi":"10.1002/cpe.70949","DOIUrl":"https://doi.org/10.1002/cpe.70949","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, a hybrid FE–SFE framework that combines distributed key generation, input masking, authenticated secret sharing, active verification, GPU acceleration, batching, compression and asynchronous communication was developed. The implementation was tested on a 10-node CPU-GPU cluster with Adult, MNIST and controlled benchmark vectors, 1000–10,000 records, and various security levels, with 2–10 parties. The framework maintained exact functional correctness across 287,000 valid evaluations, and all 80,000 injected malicious deviations were incorrectly evaluated without producing invalid output. The end-to-end latency was reduced by 41.06%–41.81% compared to sequential execution, and four-stream GPU encryption achieved 18,234 records/s. The overhead of the active verification and coercion protection was moderate. The communication volume decreased by 62.70% when the security metadata was added. The strong scaling efficiency remained above 81% up to 8 nodes, but dropped below the desired 75% at 10 nodes. The proposed framework provides a viable basis for secure multi-party analytics under controlled, security-aligned experimental conditions that can be used for rigorous and reproducible evaluations, while also being functional, verified, coercion-resistant, distributed, and performant.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 18","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148871948","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":"DIBOS: Dynamic Identity-Based Ordered Signatures With Timeout-Skip for Blockchain Document Approval","authors":"Zhanlin Wang,&nbsp;Ping Zhang","doi":"10.1002/cpe.70937","DOIUrl":"https://doi.org/10.1002/cpe.70937","url":null,"abstract":"<div>\u0000 \u0000 <p>In a document approval system, multiple signers must approve a document in strict chronological order. We propose a blockchain-based document approval system that integrates a dynamic identity-based ordered signature (DIBOS) scheme built on Sakai's identity-based signature. The DIBOS scheme supports two critical real-world features: (i) <i>dynamic signer updates</i> (insertion, deletion, or replacement of signers mid-approval while preserving prior signature validity) and (ii) <i>timeout-skip with backup signers</i> (automatic skipping of unresponsive signers with immutable audit logging). We formalize a strengthened <i>Order-Unforgeability with Skip</i> (OUf-S) security model and prove our scheme secure under it. Three smart contracts handle signer registration, document request delivery, and batch verification. We implement the off-chain signing on both Intel X64 and ARM64 platforms; dynamic operations add less than 12% overhead, making the system suitable for resource-constrained IoT devices. A full cost analysis on the Ethereum Goerli testnet demonstrates the practicality of the approach.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 18","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872021","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":"TK-SMF: Top-K Semantic Mask Fusion With Dynamic Budget and Recovery Against Adversarial Patch Attacks Under Semantic Fragmentation in Vision Models","authors":"İbrahim Yaylalı,&nbsp;Alper Kılıç","doi":"10.1002/cpe.70908","DOIUrl":"https://doi.org/10.1002/cpe.70908","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning-based vision models have severe security vulnerabilities against physical adversarial patch attacks. Previous studies propose explainability-based defense mechanisms (e.g., PatchOut and SALIUITL). These methods aim to recover the system by masking the regions that affect model decisions the most. However, these approaches generally assume that the adversarial effect is located in a single, continuous region (single mask selection). In this study, we empirically demonstrate the “Semantic Fragmentation” and saliency bleeding problems caused by multiple scattered patches in explainability maps. To overcome this issue, we propose the “Top-K Semantic Mask Fusion with Dynamic Budget (TK-SMF)” system. The proposed framework initially extracts explainability maps using LayerGradCAM, subsequently constructs a pool of candidate semantic segments via the Fast Segment Anything Model (FastSAM), and ultimately fuses the Top-K highest-risk masks within a strictly fixed area budget. This budget is anchored to a predefined worst-case threat assumption, eliminating the need to know the attacker's actual patch count during inference. We then restore the selected masked areas using OpenCV Inpainting to achieve model recovery. We evaluated the proposed framework on 38 distinct and challenging physical-world scenarios, encompassing diverse tactical vehicle categories under single- and multipatch attacks. Experimental results prove that TK-SMF breaks the targeted attack illusion with a 100% success rate. The results show that dynamic and flexible defense architectures exceed the limits of the single-mask assumption. They significantly improve model accuracy and reliability against complex physical world threats.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848907","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":"PDCFL: Prototype-Driven Decoupled Clustered Federated Learning for Non-IID Data","authors":"Yazhi Liu,&nbsp;Bo Hong,&nbsp;Zhigang Yang,&nbsp;Wei Li","doi":"10.1002/cpe.70922","DOIUrl":"https://doi.org/10.1002/cpe.70922","url":null,"abstract":"<div>\u0000 \u0000 <p>Federated Learning (FL) is prone to convergence instability and degraded personalization capability under non-independent and identically distributed (non-IID) data conditions. This article proposes a prototype-driven decoupled clustered federated learning (PDCFL) framework to address the limitations of existing prototype-based methods in terms of clustering robustness, prototype discriminability, and cluster-level personalization. Hybrid clustering based on the prototype method is proposed, which combines DBSCAN to filter out low-density client prototypes and leverages spectral clustering to capture nonlinear relationships in the prototype space, thereby achieving robust client clustering. However, to mitigate the suboptimal global knowledge caused by prototype discrimination degradation, we devise a prototype space optimization mechanism based on NT-Xent that alleviates insufficient prototype discriminability by promoting intra-cluster aggregation and increasing intercluster distance. Furthermore, an adaptive model decoupling strategy is introduced to address the remaining intra-cluster heterogeneity. This strategy dynamically adjusts the ratio of shared parameters to personalized parameters according to the similarity between client prototypes and cluster center prototypes, thereby constructing a fine-grained personalized model for each client. On the Digits-5 dataset under the feature &amp; label shift setting, PDCFL achieves an accuracy improvement of 3.52%; under the label shift setting, the method achieves an average improvement of 9.05% across five domains.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848949","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":"DAV-Diff: A Diffusion Model for High-Quality Remote Sensing Image Reconstruction Based on Multi-Level Attention and \u0000 \u0000 \u0000 v\u0000 \u0000 $$ v $$\u0000 -Prediction","authors":"Shanxu Wu,&nbsp;Lihua Tian,&nbsp;Chen Li","doi":"10.1002/cpe.70913","DOIUrl":"https://doi.org/10.1002/cpe.70913","url":null,"abstract":"<div>\u0000 \u0000 <p>With the advancement of deep learning, diffusion models have gained prominence as a powerful method for high-fidelity image generation by progressively removing noise to restore structural and textural details. In remote sensing image super-resolution (RSISR), which typically utilizes paired high-resolution (HR) and low-resolution (LR) images for supervised training, applying diffusion models directly faces three persistent challenges. First, common preprocessing techniques such as bicubic interpolation lose substantial prior information, resulting in a lack of high-frequency details and weakened spatial structural features. Second, existing denoising networks often exhibit insufficient global modeling capability, performing poorly in capturing long-range spatial relationships and evaluating channel-wise feature importance. Third, traditional noise prediction (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϵ</mi>\u0000 </mrow>\u0000 <annotation>$$ epsilon $$</annotation>\u0000 </semantics></math>-prediction) struggles to restore fine details under high-noise conditions, frequently failing to preserve the complex structures of ground objects in remote sensing scenes. To address these issues, this paper proposes DAV-Diff, a fast-sampling conditional diffusion model for RSISR. DAV-Diff coordinates three task-oriented components to improve conditional prior representation, long-range dependency modeling, and high-noise detail recovery in RSISR. The first is a multi-dilated residual attention block (MD-RAB), which enhances the extraction of prior information from LR images and compensates for detail loss caused by interpolation. The second component is a dual-attention mechanism embedded within the denoising network to strengthen long-range spatial dependency modeling and adaptive channel feature weighting. The third is a residual-domain <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>v</mi>\u0000 </mrow>\u0000 <annotation>$$ v $$</annotation>\u0000 </semantics></math>-prediction strategy that replaces traditional noise prediction, mitigating detail degradation in high-noise environments while preserving structural features of complex ground objects. Experimental results show the effectiveness of DAV-Diff against state-of-the-art methods; ablation studies, computational complexity analysis, and sampling-step sensitivity experiments further quantify the contribution and efficiency of each component.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848948","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":"From Source Code to Cost: A Multilingual, Cost-Aware Runtime Prediction Framework for Multi-Cloud FaaS","authors":"Leonardo Rebouças de Carvalho,&nbsp;Geraldo Pereira Rocha Filho,&nbsp;Aleteia Araujo","doi":"10.1002/cpe.70928","DOIUrl":"https://doi.org/10.1002/cpe.70928","url":null,"abstract":"<p>Predicting the runtime and cost of Function-as-a-Service (FaaS) applications remains challenging in multi-cloud environments due to variations in code complexity, workload characteristics, and provider-specific behaviors. This paper presents an extended version of the Orama Framework that advances runtime prediction toward a multilingual and cost-aware approach. The framework incorporates a multilingual Halstead metric extractor for language-agnostic static analysis and enhances the predictor to estimate execution costs by combining runtime forecasts with cloud pricing models across AWS Lambda, Google Cloud Functions, Azure Functions, and Alibaba Function Compute. To assess robustness and generalization, the data set is expanded with a scientific workload based on genetic sequence alignment, introducing input-sensitive execution patterns. The extended data set integrates static code metrics, workload scale, infrastructure metadata, and empirical multi-cloud execution traces. Neural network models (Dense, LSTM, and BLSTM) are retrained and evaluated using standard regression metrics and cost estimation. Results indicate that the enhanced BLSTM model maintains high predictive precision across heterogeneous workloads and providers, while enabling cross-cloud cost estimation directly from source code. The extended framework provides a unified approach for performance and cost-aware prediction in serverless computing environments.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848619","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":"TF-LB-CLPAS: Trapdoor-Free Lattice-Based Certificateless Public Auditing Scheme for Cloud-Assisted IoT","authors":"Renuka Cheeturi,&nbsp;Syam Kumar Pasupuleti,&nbsp;Rashmi Ranjan Rout","doi":"10.1002/cpe.70923","DOIUrl":"https://doi.org/10.1002/cpe.70923","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud-assisted IoT systems increasingly rely on certificateless public auditing (CLPA) mechanisms to ensure the integrity of outsourced data stored in the cloud while avoiding certificate management and key escrow issues. However, most existing CLPA schemes are built on conventional cryptographic assumptions and are therefore vulnerable to quantum attacks. To resist quantum adversaries, several lattice-based CLPA schemes (LB-CLPAS) have been proposed. Nevertheless, these schemes typically rely on complex lattice trapdoor constructions and adopt integer or ideal lattices, resulting in high computational overhead and rendering them impractical for resource-constrained IoT systems. To address these limitations, this paper proposes a trapdoor-free lattice-based certificateless public auditing scheme for cloud-assisted IoT (TF-LB-CLPAS). By eliminating complex trapdoors and adopting module lattices, the scheme significantly improves computational efficiency while maintaining strong post-quantum security. Security is formally proven under the Module-SIS and Module-LWE assumptions. Performance evaluations demonstrate lower computational overhead and superior practicality compared to existing trapdoor-based LB-CLPAS, making it well suited for cloud-assisted IoT devices.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848812","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":"Manifold Regularized Nuclear Norm Matrix Group Sparse Classifier for Robust Image Classification","authors":"Caikou Chen,&nbsp;Qian Peng,&nbsp;Xiaoguang Qiao","doi":"10.1002/cpe.70927","DOIUrl":"https://doi.org/10.1002/cpe.70927","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Robust representation-based image classification must cope with complex mixed degradations, including global structural variations (e.g., severe non-linear illumination) and localized contiguous corruptions (e.g., block occlusions or disguises). While existing matrix-based low-rank regression methods effectively model structural residuals without breaking the intrinsic two-dimensional spatial correlations of pixels, they consistently neglect the underlying geometric relationships among training samples. This oversight leads to dense and weakly discriminative representation coefficients, particularly in under-determined small sample size (SSS) scenarios. To address this limitation, we propose the Manifold Regularized Nuclear Norm Matrix Group Sparse Classifier (MNMGSC), a unified convex matrix optimization framework. MNMGSC jointly enforces a low-rank residual via a nuclear-norm penalty, explicitly models contiguous occlusion via a matrix &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ℓ&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{ell}}_{2,1} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; structural penalty, promotes class-wise discriminative coefficients via a group-sparse &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ℓ&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{ell}}_{2,1} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-norm, and ensures geometric consistency via a Graph Laplacian regularizer. This mathematical design explicitly yields a formal &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ℓ&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathrm{ell}}_{2,1} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-spectrum that bridges sparse and collaborative representation paradigms. To efficiently solve the proposed non-smooth model, we develop a Nesterov-accelerated Alternating Direction Method of Multipliers (ADMM) and provide explicit theoretical guarantees, including a Group Sparse Low-Rank Restricted Isometry Property (GS-LRIP) recovery error bound and convergence proof. Evaluated extensively on the ORL, Extended Yale B, AR Face, and MNIST databases against both the unregularized baseline NMGSC and recent s","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848675","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":"HECO-PSO: Hierarchical and Stagnation-Aware Particle Swarm Optimization With Elite-Guided Crossover","authors":"Haopeng Lei,&nbsp;Jiahui Fan,&nbsp;Jihua Ye,&nbsp;Aiwen Jiang,&nbsp;Mingwen Wang","doi":"10.1002/cpe.70915","DOIUrl":"https://doi.org/10.1002/cpe.70915","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a Hierarchical and Stagnation-Aware Particle Swarm Optimization with Elite-Guided Crossover (HECO-PSO). HECO-PSO dynamically partitions the swarm into multiple parallel layers, enabling independent exploration and periodic information exchange to mitigate premature convergence. Within each layer, elite-guided crossover integrates a particle's best position with that of the layer's elite particle, generating high-quality guiding vectors to enhance solution quality and maintain diversity. Building on this hierarchical framework, a stagnation-aware strategy adaptively intensifies guidance: prolonged stagnation activates crossover with the global elite, while severe stagnation directly assigns the global best guiding vector, thereby strengthening the ability to escape local optima. Experimental results on the CEC 2017 benchmark suite demonstrate HECO-PSO's competitive convergence speed and solution accuracy. Its effectiveness is further validated through a real-world UNSW-NB15 feature selection task, confirming adaptability and competitiveness in practical applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148785020","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":"Piranha Predation Algorithm: A Bio-Inspired Metaheuristic for Solving High-Dimensional Complex Real-World Optimization Problems","authors":"Qiang Peng,&nbsp;Husheng Wu,&nbsp;Zizheng Han","doi":"10.1002/cpe.70920","DOIUrl":"https://doi.org/10.1002/cpe.70920","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes the Piranha Predation Algorithm (PPA), a population-based metaheuristic inspired by the sensory and group-response behaviors of piranhas. The algorithm combines four candidate-generation mechanisms associated with visual, auditory, olfactory, and lateral-line perception with population interaction and best-guided local refinement. These operations provide local sampling, directed search, stochastic perturbation, and diversity maintenance to balance exploration and exploitation. Evaluations on the CEC-2020 and CEC-2022 benchmark suites show that PPA is competitive with 14 comparison algorithms and performs favorably on many tested functions, although it is not uniformly the best method across all functions and dimensions. Tests on five constrained engineering design problems and four three-dimensional UAV path-planning scenarios further indicate its practical potential under the studied settings.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 17","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784417","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}