Applied Soft Computing最新文献

{"title":"Boundary-guided large-scale vision model for unified multi-domain industrial anomaly detection","authors":"Zixuan Zhang ,&nbsp;Fan Shi ,&nbsp;Chen Jia ,&nbsp;Mianzhao Wang ,&nbsp;Xu Cheng","doi":"10.1016/j.asoc.2026.114650","DOIUrl":"10.1016/j.asoc.2026.114650","url":null,"abstract":"<div><div>Extracting shared boundary cues across different anomaly domains is critical to enhancing generalization on unseen data, thereby laying the foundation for unified industrial anomaly detection paradigms. Existing unified detection paradigms often directly extract discriminative features from multiple data domains. However, due to the inherent semantic gaps between different data sources, bridging this disparity within a shared feature representation across multiple data domains remains a key challenge. To address this challenge, we propose a boundary-guided large-scale vision model that extracts commonalities across diverse domains. Specifically, we generate initial feature embeddings by establishing a multi-domain normal sample repository and employing a parameter coupling strategy. This captures shared boundary information across different data domains, thereby reducing the inherent semantic gaps. For anomalous feature synthesis, we incorporate this boundary information into the generation process, ensuring that the synthesized features retain critical structural details while expanding the coverage of potential anomalous data distributions. Additionally, to enhance feature space separation between normal and anomalous samples, we introduce a hybrid constraint optimization mechanism that improves the discriminative ability of the model. Extensive experiments on the MVTec AD, VisA, and MPDD datasets demonstrate that our method achieves state-of-the-art performance across various industrial scenarios. Experimental results demonstrate the effectiveness of boundary-guided shared information for multi-domain anomaly detection.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114650"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"End-to-end discrete cosine transform integration in spectral convolutional neural networks for resource-efficient deep learning","authors":"Ibrahim Yousef Alshareef ,&nbsp;Ab Al-Hadi Ab Rahman ,&nbsp;Nuzhat Khan ,&nbsp;Hasan Alqaraghuli","doi":"10.1016/j.asoc.2026.114599","DOIUrl":"10.1016/j.asoc.2026.114599","url":null,"abstract":"<div><div>Spectral convolutional neural networks using Fast Fourier Transform (FFT) often suffer from high computational complexity and memory demands due to complex valued operations and the need for inverse transforms limiting their deployment on resource constrained devices. This paper presents a novel end-to-end spectral convolutional neural network (SpCNN) architecture that operates entirely in the Discrete Cosine Transform (DCT) domain, eliminating the need for inverse transformations and complex arithmetic. Leveraging the DCT’s real valued representation and superior energy compaction, the proposed design significantly reduces computational workload and memory usage while preserving classification accuracy. Key innovations include the removal of IFFT layers, a frequency domain adaptive activation function (FReLU), and a DCT optimized spectral pooling mechanism, each tailored for deployment in low power, resource constrained environments. Experimental evaluations on MNIST and a 94-class ASCII dataset demonstrate the model’s efficiency: LeNet5-DCT achieves a 37.96% FLOPs reduction, 18.45% lower memory usage, and 96.56% test accuracy, while VGG7-DCT achieves a 33.95% FLOPs reduction, 14.32% lower memory usage, and 90.62% test accuracy. The architecture also shows strong robustness to quantization, confirming its suitability for edge AI applications and low energy inference. This work provides a scalable, hardware efficient spectral learning framework, paving the way for future hybrid spectral models optimized for embedded environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114599"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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-view feature representation learning: Integrating medical team information to enhance online physician recommendation","authors":"Yongbo Ni,&nbsp;Donghui Yang","doi":"10.1016/j.asoc.2026.114776","DOIUrl":"10.1016/j.asoc.2026.114776","url":null,"abstract":"<div><div>Recommending suitable physicians to patients in online healthcare communities remains a challenging decision-making task. While existing studies leverage physicians’ professional features (clinical expertise) or service features (patient evaluations) for recommendations, single-view feature analysis and limited physician-patient interaction data often lead to insufficient feature representation, which further results in relatively poor recommendation performance. The emergence of online medical teams (OMTs) provides patients with a novel team perspective and data source for understanding physician features. Therefore, this study aims to leverage the OMTs information to construct a multi-view feature representation learning framework to enhance the physician feature representation and improve the recommendation performance. We first integrate the OMTs information with online reviews, disease descriptions, and physician profiles to build the multi-source and multi-type medical data inputs. Based on these inputs, we leverage BERT, TextCNN, semantic analysis, and graph neural network technologies to construct the multi-view feature representation learning framework from the views of professional features, service features, and team features. Then, these multi-view features are fused through a multilayer perceptron to predict the selection probabilities. Experimental results demonstrate the multi-view features based method incorporating OMTs information shows an average improvement of 7.5 % in HR@5, 5.3 % in NDCG@5, and 7.8 % in MRR compared to other classical methods. Surprisingly, the physician service capabilities involved in the team features have a greater impact on the recommendation performance than the physician professional capabilities involved.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114776"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Evolving explainable neural architectures: Dynamic behavioral-novelty weighting in multi-objective neuroevolution for multimodal spam detection","authors":"Adnane Filali ,&nbsp;El Arbi Abdellaoui Alaoui ,&nbsp;Mostafa Merras","doi":"10.1016/j.asoc.2026.114790","DOIUrl":"10.1016/j.asoc.2026.114790","url":null,"abstract":"<div><div>Spam detection is effectively a moving target: as spammers shift their strategies across images, voice calls, and text, static detection systems struggle to keep pace. Artificial neural architectures, though often accurate, frequently fail to strike a workable balance between performance, generalization, and behavioral novelty in such adversarial environments. In this paper, we introduce and analyze two multi-objective variants of NeuroEvolution of Augmenting Topologies: a static weighting approach (NEAT-stMO) and a dynamic version (NEAT-dyMO) that adaptively reallocates objective priorities during the evolutionary process. Both methods simultaneously optimize for accuracy, the generalization gap, and novelty while evolving the topology and hyperparameters of artificial neural networks. We benchmark these evolutionary strategies against a broad spectrum of baselines, ranging from Pareto-based and single-objective NEAT variants to conventional classifiers, MLP, SVM, and automated deep learning frameworks, including Vision Transformers, Auto-Keras, and Hyperband. Our evaluation spans three distinct modalities—image, voice, and imbalanced SMS data—using five-fold cross-validation to rigorously assess accuracy, F1-scores, AUC, and computational efficiency—time and RAM. Importantly, we integrate SHapley Additive exPlanations to deconstruct the “black box” of how hyperparameters drive performance. Empirical results place NEAT-dyMO consistently among the top performers; notably, it offers significant efficiency advantages over heavy Transformer models while surpassing static baselines. SHAP analysis further sheds light on the mechanism, exposing that NEAT-dyMO’s dynamic weighting successfully navigates the trade-off between exploration—driven by larger architectures and dropout—and exploitation—favored by small batch sizes. These findings, therefore, suggest that dynamically evolving ANNs provide a robust, adaptive, and interpretable alternative for real-world multimodal spam defense.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114790"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"DMEL: A novel dual-modal ensemble learning architecture for multi-step runoff prediction","authors":"Wen-chuan Wang ,&nbsp;Miao Gu,&nbsp;Zong Li,&nbsp;Yi-yang Wang,&nbsp;Yan-wei Zhao","doi":"10.1016/j.asoc.2026.114792","DOIUrl":"10.1016/j.asoc.2026.114792","url":null,"abstract":"<div><div>Accurate multi-step runoff prediction is crucial for effective water resource management and flood disaster reduction. Despite significant advancements in deep learning for hydrological predictions, identifying the modal characteristics of runoff sequences and extending the effective lead time of prediction models remain challenging. This paper introduces a novel Dual-Modal Ensemble Learning (DMEL) architecture that addresses these issues by integrating a Modal Recognition Strategy (MRS) with two deep learning models: Informer and Long Short-Term Memory (LSTM). The MRS technique first reduces the nonlinearity and non-stationarity of runoff data through feature extraction and fusion, lowering model computational complexity and mitigating cumulative errors from high-dimensional inputs. The optimized modal components are then categorized into high-frequency and low-frequency components, which are processed by the Informer and LSTM models, respectively, leveraging their unique capabilities for dual-modal ensemble learning. The final prediction is obtained by superimposing the results from all sequences. The two sites’ analysis results demonstrate the DMEL architecture’s effectiveness. Taking the Shuangpai Station as an example, in single-step prediction, the Kling-Gupta Efficiency (KGE) index of the DMEL model achieves relative improvements of 22.60 %, 28.32 %, 25.34 %, 43.90 %, 45.06 %, and 42.30 % compared to the Informer model, Transformer model, LSTM model, Recurrent Neural Network (RNN) model, TimesNet model, and PatchTST model, respectively. When the prediction step increases to the seventh step, the KGE index of the DMEL model still shows relative increases of 37.50 %, 105.71 %, 89.84 %, 146.04 %, 211.82 %, and 178.06 % compared to the Informer model, Transformer model, LSTM model, RNN model, TimesNet model, and PatchTST model, respectively. This indicates that the DMEL architecture can accurately capture critical information in daily runoff sequences, extending the model’s effective prediction period and providing a robust scientific basis for water resource management and flood disaster reduction.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114792"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"MSCNRNet: Non-dense residual network with multi-skip connection for image restoration","authors":"Yun Chen ,&nbsp;Huanhuan Luo ,&nbsp;Mengnan Shi ,&nbsp;Xiaowen Ma ,&nbsp;Hongtao Li ,&nbsp;Qiang Yao","doi":"10.1016/j.asoc.2026.114742","DOIUrl":"10.1016/j.asoc.2026.114742","url":null,"abstract":"<div><div>In recent years, residual dense network(RDN)-based models have become increasingly popular in image restoration (IR) due to their enhanced feature propagation and ability to resist overfitting. However, these models still have limitations: 1) Their convolutional blocks rely on dense connections, leading to redundant local feature capture and repetitive features. 2) Each layer in the block is a single convolutional layer, limiting representational power and local feature extraction. 3) Basic connections between blocks hinder effective global feature fusion, resulting in reconstructed images lacking details. To address the limitations of RDN-based models, this paper proposes a novel IR model Non-dense Residual Network with Multi-skip Connection for Image Restoration (MSCNRNet). It replaces traditional dense connections in RDNs with multi-skip connections to form multi-skip connection non-dense residual blocks (MSCNRBs), reducing redundant features. Each MSCNRB comprises two multi-skip connection convolutional layers (MSCNRConv) to enhance local feature extraction, with a feature enhancement layer (FEL) added after each block for better global feature fusion. Based on extensive experiments across 12 synthetic and real-world datasets, our MSCNRNet model outperforms current state-of-the-art (SOTA) models in image restoration (IR) tasks by approximately 0.1% to 5% in the peak signal-to-noise ratio (PSNR) and around 2% in the structural similarity index measure (SSIM). This work provides an efficient solution to the limitations of existing RDN-based models, offering a more effective approach for high-quality IR tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114742"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Robustness and invariance of hybrid metaheuristics under objective function transformations","authors":"Grzegorz Sroka ,&nbsp;Sławomir T. Wierzchoń","doi":"10.1016/j.asoc.2026.114799","DOIUrl":"10.1016/j.asoc.2026.114799","url":null,"abstract":"<div><div>This paper evaluates the robustness and structural invariance of hybrid population-based metaheuristics under various objective space transformations. A light-weight plug-and-play hybridization operator is applied to nineteen state-of-the-art algorithms – including differential evolution, particle swarm optimization, and recent bio-inspired methods – without modifying their internal logic. Benchmarking on the CEC-2017 suite across four dimensions (10, 30, 50, 100) is performed under five transformation types: baseline, translation, scaling, rotation, and constant shift. Statistical comparisons based on Wilcoxon and Friedman tests, Bayesian dominance analysis, and convergence trajectory profiling consistently show that differential-based hybrids (e.g., hybrid improved multi-operator differential evolution, hybrid success-history adaptive differential evolution, hybrid double mutational salp swarm algorithm) maintain high accuracy, stability, and invariance across all tested deformations. In contrast, classical algorithms – especially particle swarm optimization- and Harris hawks optimization - based variants –exhibit significant performance degradation under non-separable or distorted landscapes. The findings confirm the superiority of adaptive, structurally resilient hybrids for real-world optimization tasks subject to domain-specific transformations. However, the evidence is based on deterministic, continuous, bound-constrained single-objective CEC-2017 functions. The experiments use a fixed budget and five predefined transformations. Therefore, the conclusions should be interpreted within this controlled setting and may not directly generalize to nonlinear constrained, noisy, dynamic, or multi-objective problems without dedicated protocols. To partially bridge to practice, we also include a constrained helical compression spring design case study evaluated with the same methodology.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114799"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Dynamic weight matching TEB local path planning improved by PSO for car-like robots","authors":"Yunlong Wang ,&nbsp;Annan Tang ,&nbsp;Wenfeng Li ,&nbsp;Shaoke Wan ,&nbsp;Rongcan Qiu ,&nbsp;Xiaohu Li","doi":"10.1016/j.asoc.2026.114783","DOIUrl":"10.1016/j.asoc.2026.114783","url":null,"abstract":"<div><div>Local path planning for car-like robots often relies on the Timed Elastic Band (TEB) algorithm, yet conventional implementations suffer from two critical limitations: insufficient trajectory smoothness resulting in jerky motion and mechanical wear, and high sensitivity to weight parameters requiring laborious manual tuning that fails to generalize across diverse scenarios. To address these challenges, this paper proposes a novel framework that enhances TEB through explicit smoothness constraints while eliminating manual tuning via Particle Swarm Optimization (PSO). We first augment the TEB objective function with higher-order smoothness and jerk constraints to fundamentally improve trajectory quality and dynamic feasibility. Second, we introduce a systematic offline-online framework where PSO discovers optimal weight configurations across 150 representative training scenarios, each characterized by a three-dimensional feature vector capturing narrowness, turning complexity, and heading deviation. The discovered scenario-weight mappings are stored in a knowledge base enabling real-time weight retrieval through efficient similarity matching during online operation. Comprehensive evaluation across three progressively challenging test scenarios with 90 independent runs demonstrates statistically significant improvements: PSO-TEB achieves 34–38% execution time reduction, 39–53% acceleration smoothness improvement, and 100% success rate in dynamic environments compared to 0–20% for baseline methods. Real-world experiments on a car-like robotic platform validate practical viability with sub-millisecond online weight matching overhead. By shifting computational burden offline and enabling scenario-adaptive parameter adjustment, our approach enhances robustness and practical applicability of TEB for car-like robots in complex real-world conditions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114783"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"3D-NoduleNet: A comprehensive framework for benign and malignant pulmonary nodule classification","authors":"Huihui Yu ,&nbsp;Qun Dai ,&nbsp;Yanfu Wu","doi":"10.1016/j.asoc.2026.114645","DOIUrl":"10.1016/j.asoc.2026.114645","url":null,"abstract":"<div><div>Accurate pulmonary nodule classification is essential for lung cancer diagnosis but poses significant challenges due to class imbalance, high appearance heterogeneity, and subtle inter-class variations. To tackle these challenges, this paper proposes 3D-NoduleNet, a clinical-semantics-driven, unified framework that integrates detection and segmentation into a task-oriented pipeline for nodule classification. This framework begins with the Region Proposal and Segmentation Module (RPSM), which adopts a detection-guided segmentation strategy. The region proposals generated by the 3D Faster R-CNN are used to crop ROIs, which are subsequently segmented by the 3D BCDU-Net into aligned, morphologically consistent masks—explicitly instantiating key clinical semantic cues: texture and morphology. Next, the Multi-view Feature Extraction Module (MFEM) employs a dual-path architecture under a refine-then-fuse scheme. Within each path, an internal cascade refines view-specific semantic features through multi-scale contextual aggregation and sequential attention, whose output is fused to leverage complementary clinical cues. Finally, the Pulmonary Nodule Classification Module (PNCM) adopts a dual-supervision strategy: the binary cross-entropy loss provides primary instance-level supervision, while the auxiliary supervised contrastive loss regularizes the feature space by clustering same-class nodules and separating different-class ones—enhancing separability and improving benign-malignant classification. Through this coordinated semantic flow, the modules form a tightly integrated framework, systematically transforming clinical semantics from instantiation to discrimination. Experiments on LIDC-IDRI demonstrate the superior and well-balanced performance of 3D-NoduleNet over existing methods, with achievements of 92.50% sensitivity and 90.63% F1-score, alongside competitive accuracy and specificity. Ablation studies validate the effectiveness of key components and strategies, highlighting its robustness and clinical applicability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114645"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"An adaptive multi-stage clustering-based differential evolution algorithm with dynamic niching for robust solving of high-dimensional nonlinear equation systems","authors":"Wulfran Fendzi Mbasso ,&nbsp;Hassan M. Hussein Farh ,&nbsp;Ambe Harrison ,&nbsp;Abdullrahman A. Al-Shamma","doi":"10.1016/j.asoc.2026.114666","DOIUrl":"10.1016/j.asoc.2026.114666","url":null,"abstract":"<div><div>Solving high-dimensional systems of Nonlinear Equation Systems (NES) requires search procedures that preserve diversity while converging reliably and, where applicable, discovering multiple roots. We propose the Adaptive Multi-stage Clustering Differential Evolution (AMCDE) algorithm, which couples fitness-aware clustering with entropy-triggered dynamic niching and feeds cluster-level convergence signals into mutation/crossover control in Differential Evolution (DE). We evaluate AMCDE on 110 NES benchmarks under a matched computation budget (population size of 100 and 10,000 function evaluations) and a uniform success threshold ‖F(x)‖₂ ≤ 10⁻⁸ (relaxed to 10⁻⁶ for flagged ill-conditioned subsets). Distinct-root discovery is quantified using Euclidean (ℓ₂) distance–based deduplication at 10⁻³ (optional pre-cluster at 10⁻⁴). We compare against recent NES-oriented competitors and adaptive DE baselines, including JADE (Adaptive Differential Evolution with Optional External Archive) and SHADE (Success-History Adaptive Differential Evolution), re-implemented on the same platform with grid-tuned settings for fairness. AMCDE attains the best median success rate and residuals, with higher distinct-root coverage at comparable runtimes; nonparametric Wilcoxon tests and Friedman tests with Holm correction confirm significance. A critical-difference diagram places AMCDE in the top clique. These findings indicate that coupling adaptive clustering with dynamic niching yields a robust, scalable NES solver that preserves exploration without sacrificing convergence efficiency. All comparative results are validated using non-parametric statistics (Wilcoxon signed-rank and Friedman tests with Holm post-hoc, α=0.05), with our method showing statistically significant gains over competing DE variants across the benchmark suite.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114666"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}