Complex & Intelligent Systems最新文献_第4页

Analysis of quantum fully homomorphic encryption schemes (QFHE) and hierarchial memory management for QFHE 量子完全同态加密方案分析及QFHE的分层内存管理

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-23 DOI: 10.1007/s40747-025-01851-7

Shreya Savadatti, Aswani Kumar Cherukuri, Annapurna Jonnalagadda, Athanasios V. Vasilakos

{"title":"Analysis of quantum fully homomorphic encryption schemes (QFHE) and hierarchial memory management for QFHE","authors":"Shreya Savadatti, Aswani Kumar Cherukuri, Annapurna Jonnalagadda, Athanasios V. Vasilakos","doi":"10.1007/s40747-025-01851-7","DOIUrl":"https://doi.org/10.1007/s40747-025-01851-7","url":null,"abstract":"Homomorphic encryption is a recent and fundamental breakthrough in modern cryptography, which allows the performance of operations on encrypted data without unveiling the data. Leveraging quantum mechanics principles, quantum computers can potentially solve certain computational problems exponentially faster than classical computers. This immense computational power offers new possibilities for various fields, including cryptography. The rapid evolution of both these fields has led to the development of quantum fully homomorphic encryption (QFHE), which makes the capabilities of classical HE extend into the quantum domain. However, many existing QFHE schemes require significant memory due to complex calculations and fault-tolerance needs. This paper contributes in two ways. First, we provide a comprehensive survey of two specific QFHE schemes, discussing their underlying principles, mathematical frameworks, security aspects, and practical applications. We also explore the challenges posed by quantum computing and how QFHE addresses these to achieve both security and computational efficiency. Second, we propose a new hierarchical memory management system for QFHE, which includes a “quantum cache” (a specialized memory storage for quantum data) and a “reinforcement learning agent” (an intelligent system that learns from experience to optimize decisions). This system dynamically manages data movement between the cache and classical memory, improving memory efficiency and potentially boosting computational performance.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"35 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An extended TOPSIS technique based on correlation coefficient for interval-valued q-rung orthopair fuzzy hypersoft set in multi-attribute group decision-making 多属性群决策中基于相关系数的区间值q阶正形模糊超软集扩展TOPSIS技术

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-23 DOI: 10.1007/s40747-025-01838-4

Rana Muhammad Zulqarnain, Imran Siddique, Sameh Askar, Ahmad M. Alshamrani, Dragan Pamucar, Vladimir Simic

{"title":"An extended TOPSIS technique based on correlation coefficient for interval-valued q-rung orthopair fuzzy hypersoft set in multi-attribute group decision-making","authors":"Rana Muhammad Zulqarnain, Imran Siddique, Sameh Askar, Ahmad M. Alshamrani, Dragan Pamucar, Vladimir Simic","doi":"10.1007/s40747-025-01838-4","DOIUrl":"https://doi.org/10.1007/s40747-025-01838-4","url":null,"abstract":"The accurate determination of results in decision analysis is usually predicated on the association between two factors. Although generating data for analytical purposes presents an apparent hurdle, the data obtained may present hurdles in its interpretation. Correlation coefficients can be used to analyze the interaction between two factors and their variations. These coefficients deliver an objective description of the association between parameters, assisting in predicting and assessing alterations between particular parameters. The purpose of this research is to explore the applicability of correlation coefficients (CC) and weighted correlation coefficients (WCC) in interval-valued q-rung orthopair fuzzy hypersoft sets (IVq-ROFHSS) structures with their essential characteristics. These measures are developed to address the inevitable confusion, inconsistency, and volatility in real-life decision-making challenges. The implementation of these components attempts to boost the productivity of the technique for order preference by similarity to the ideal solution (TOPSIS) method. The computational models with correlation constraints are presented to determine the reliability and regularity of the proposed method. This research proves that the proposed technique is effective for multi-attribute group decision-making (MAGDM), particularly for analyzing and prioritizing convoluted data sets. Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in reality in evaluating bio-medical disposal techniques for hospitals. This study determines incineration as the most beneficial method for BMW disposal, demonstrating its more efficient use of alternative disposal techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative estimation method for complex part surface defects based on multimodal information fusion 基于多模态信息融合的复杂零件表面缺陷定量估计方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-23 DOI: 10.1007/s40747-025-01874-0

Rui Wang, Wei Du, Qingchao Jiang

{"title":"Quantitative estimation method for complex part surface defects based on multimodal information fusion","authors":"Rui Wang, Wei Du, Qingchao Jiang","doi":"10.1007/s40747-025-01874-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01874-0","url":null,"abstract":"Surface quality is critical for the performance of high-end equipment, with defects potentially leading to severe operational failures. Current defect detection methods face challenges: 2D imaging lacks the ability to capture scratch depth, limiting quantitative damage assessment, while 3D point cloud methods are costly and time-consuming, hindering scalability in manufacturing. This study proposes a multimodal defect detection system (MDDS) that merges the benefits of 2D imaging and 3D point clouds for comprehensive defect analysis on complex parts. Utilizing a binocular vision system with high-precision industrial cameras, the system captures detailed 2D images and generates 3D point clouds through advanced reconstruction techniques. We enhance the Faster R-CNN network to improve defect localization and feature extraction, establishing a mapping between 2D images and 3D data to pinpoint defect-specific areas accurately. Additionally, we introduce a novel feature extraction approach using normal vector aggregation and the Fast Point Feature Histogram (FPFH) descriptor, combined with fuzzy C-means clustering, to detect and quantify scratch defects. This method assesses defect dimensions and depth, enabling precise damage classification. Tested on aero-engine impeller parts, our approach has proven effective in identifying and quantifying scratch defects on complex industrial components. The results demonstrate the system’s applicability and efficiency, making it a viable solution for practical implementation in industrial environments.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"216 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke M2FNet：用于急性和亚急性缺血性脑卒中分割的多模态多层次融合网络

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-23 DOI: 10.1007/s40747-025-01861-5

Shannan Chen, Xuanhe Zhao, Yang Duan, Ronghui Ju, Peizhuo Zang, Shouliang Qi

{"title":"M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke","authors":"Shannan Chen, Xuanhe Zhao, Yang Duan, Ronghui Ju, Peizhuo Zang, Shouliang Qi","doi":"10.1007/s40747-025-01861-5","DOIUrl":"https://doi.org/10.1007/s40747-025-01861-5","url":null,"abstract":"Ischemic stroke, a leading cause of death and disability, necessitates accurate detection and automatic segmentation of lesions. While diffusion weight imaging is crucial, its single modality limits the detection of subtle lesions and artifacts. To address this, we propose a multi-modality, multi-level fusion network (M2FNet) that aggregates salient features from different modalities across various levels. Our method uses a multi-modal independent encoder to extract modality-specific features from images of different modalities, thereby preserving key details and ensuring rich features. In order to suppress noise while ensuring the best preservation of modality-specific information, we effectively integrate features of different modalities through a cross-modal encoder fusion module. In addition, a cross-modal decoder fusion module and a multi-modality joint loss are designed to further improve the fusion of high-level and low-level features in the up-sampling stage, dynamically utilizing complementary information from multiple modalities to improve segmentation accuracy. To verify the effectiveness of our proposed method, M2FNet was validated on two public magnetic resonance imaging ischemic stroke lesion segmentation benchmark datasets. Whether single or multi-modality, M2FNet performed better than ten other baseline methods. This highlights the effectiveness of M2FNet in multi-modality segmentation of ischemic stroke lesions, making it a promising and powerful quantitative analysis tool for rapid and accurate diagnostic support. The codes of M2FNet are available at https://github.com/ShannanChen/MMFNet.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"41 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised feature selection based on generalized regression model with linear discriminant constraints 基于线性判别约束广义回归模型的无监督特征选择

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01873-1

Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai

{"title":"Unsupervised feature selection based on generalized regression model with linear discriminant constraints","authors":"Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai","doi":"10.1007/s40747-025-01873-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01873-1","url":null,"abstract":"Unsupervised feature selection (UFS) methods play a crucial role in improving the efficiency of extracting relevant information and reducing computational complexity in the context of big data analysis. Despite notable advancements in the field of unsupervised feature selection for large-scale datasets, many UFS methods still remain redundant and irrelevant features during the feature selection process. To tackle these challenges, we present a novel unsupervised feature selection method that leverages the generalized regression model with linear discriminant constraints to learn discriminant and effective features from the data. Benefited from this, the relationships and patterns within the high-dimensional data are retained in the reduced-dimensional feature space. We reformulate our proposed method as a multi-variable optimization problem that incorporates equality constraints. To efficiently solve this problem, we develop an algorithm that updates each variable alternately. Extensive experiments on six datasets among nine state-of-the-art methods on the clustering task are conducted to demonstrate the effectiveness of the proposed method.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive integrated weight unsupervised multi-source domain adaptation without source data 无源数据的自适应积分权无监督多源域自适应

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01871-3

Zhirui Wang, Liu Yang, Yahong Han

{"title":"Adaptive integrated weight unsupervised multi-source domain adaptation without source data","authors":"Zhirui Wang, Liu Yang, Yahong Han","doi":"10.1007/s40747-025-01871-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01871-3","url":null,"abstract":"Unsupervised multi-source domain adaptation methods transfer knowledge learned from multiple labeled source domains to an unlabeled target domain. Existing methods assume that all source domain data can be accessed directly. However, such an assumption is unrealistic and causes data privacy concerns, especially when the source domain labels include personal information. In such a setting, it is prohibited to minimize domain gaps by pairwise calculation of the data from the source and target domains. Therefore, this work addresses the source-free unsupervised multi-source domain adaptation problem, where only the source models are available during the adaptation. We propose trust center sample-based source-free domain adaptation (TSDA) method to solve this problem. The key idea is to leverage the pre-trained models from the source domain and progressively train the target model in a self-learning manner. Because target samples with low entropy measured from the pre-trained source model achieve high accuracy, the trust center samples are selected first using the entropy function. Then pseudo labels are assigned for target samples based on a self-supervised pseudo-labeling strategy. For multiple source domains, corresponding target models are learned based on the assigned pseudo labels. Finally, multiple target models are integrated to predict the label for unlabeled target data. Extensive experiment results on some benchmark datasets and generated adversarial samples demonstrate that our approach outperforms existing UMDA methods, even though some methods can always access source data.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"138 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DFL topology optimization based on peer weighting mechanism and graph neural network in digital twin platform 数字孪生平台中基于对等权重机制和图神经网络的 DFL 拓扑优化

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01887-9

Nguyen Anh Tuan, Atif Rizwan, Sa Jim Soe Moe, Anam Nawaz Khan, Do Hyeun Kim

{"title":"DFL topology optimization based on peer weighting mechanism and graph neural network in digital twin platform","authors":"Nguyen Anh Tuan, Atif Rizwan, Sa Jim Soe Moe, Anam Nawaz Khan, Do Hyeun Kim","doi":"10.1007/s40747-025-01887-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01887-9","url":null,"abstract":"Decentralized federated learning (DFL) represents a distributed learning framework where participating nodes independently train local models and exchange model updates with proximate peers, circumventing the reliance on a centralized orchestrator. This paradigm effectively mitigates server-induced bottlenecks and eliminates single points of failure, which are inherent limitations of centralized federated learning architectures. However, DFL encounters significant challenges in attaining global model convergence due to inherent statistical heterogeneity across nodes and the dynamic nature of network topologies. For the first time, in this paper, we present a topology optimization framework for DFL that integrates a peer weighting mechanism with graph neural networks (GNNs) within a digital twin platform. The proposed approach leverages local model performance metrics and training latency as input factors to dynamically construct an optimized topology that balances computational efficiency and model performance. Specifically, we employ Particle Swarm Optimization to derive node-specific peer weight matrices and utilize a GNN to refine the underlying mesh topology based on these weights. Comprehensive experimental analyses conducted on benchmark datasets demonstrate the superiority of the proposed framework in achieving accelerated convergence and enhanced accuracy across diverse nodes. Additionally, comparative evaluations under IID and Non-IID data distributions substantiate the robustness and adaptability of the approach in heterogeneous learning environments, underscoring its potential to advance decentralized learning paradigms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"43 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective recommendation system utilizing a multi-population knowledge migration framework 利用多人群知识迁移框架的多目标推荐系统

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01891-z

Liang Chu, Ye Tian

{"title":"Multi-objective recommendation system utilizing a multi-population knowledge migration framework","authors":"Liang Chu, Ye Tian","doi":"10.1007/s40747-025-01891-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01891-z","url":null,"abstract":"Traditional recommendation systems tend to focus on accuracy and prefer recommending popular items, resulting in non-popular items rarely being exposed to users. However, recommending non-popular items to enhance users’ novelty experience is also crucial. Currently, many researchers are dedicated to multi-objective recommendation studies. Nevertheless, existing multi-objective recommendation algorithms often exhibit poor performance on the hypervolume value (HV) metric and lack effective methods to enhance novelty within evolutionary strategies. In this paper, we propose an innovative multi-objective recommendation algorithm based on a multi-population auxiliary evolution framework, abbreviated as MOEA-MIAE. Within this framework, we design three distinct optimization paths aimed at enhancing the convergence performance of the multi-objective algorithm and improving the hypervolume value metric of results. In addition to adopting the classical genetic algorithm as the main evolutionary population, we specifically introduce two auxiliary evolutionary populations. The first auxiliary population employs an HV-based multi-parent crossover method, while the second focuses on increasing the likelihood of generating highly novel solutions during crossover operations. These three evolutionary populations achieve effective complementarity and integration of their strengths through a mutual migration strategy of solution sets. Experimental results demonstrate that the proposed model exhibits superior performance in balancing accuracy and novelty, outperforming other comparable algorithms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"108 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of air purifiers for improving the air quality index using circular intuitionistic fuzzy Heronian means 用循环直觉模糊赫氏法评价空气净化器改善空气质量指标

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01813-z

Fengyu Guo, Raiha Imran, Shi Yin, Kifayat Ullah, Maria Akram, Dragan Pamucar, Mustafa Elashiry

{"title":"Assessment of air purifiers for improving the air quality index using circular intuitionistic fuzzy Heronian means","authors":"Fengyu Guo, Raiha Imran, Shi Yin, Kifayat Ullah, Maria Akram, Dragan Pamucar, Mustafa Elashiry","doi":"10.1007/s40747-025-01813-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01813-z","url":null,"abstract":"The impact of airborne pollutants present in the environment, entering the body through breathing, can cause significant risks of respiratory and heart-related health problems for individuals. For this, different air purifiers are commonly used to eliminate delicate particulate matter PM2.5, and various studies have examined their effectiveness. This paper aims to analyze airborne pollutants and, by considering them, assess the performance of air purifiers in reducing the concentration of air pollutants in the environment. The aggregation operator (AO) plays a significant role in aggregating the multiple criteria and gives us a result in a singleton set, which assists us in decision-making (DM). So, considering this, the Heronian mean (HM) operator and its special cases such as averaging and geometric operators have been used in this paper. Moreover, circular intuitionistic fuzzy (C-IF) theory is more efficient and comprehensive than the intuitionistic fuzzy set (IFS), as the standard IFS cannot cope with the problems within a circular environment. So, the HM operator under the C-IF environment, such as circular intuitionistic fuzzy Heronian mean (C-IFHM) and it’s averaging and geometric operator, has been presented. Further, some prevalent and crucial properties and theorems have been defined. Then, an algorithm was developed to solve a multicriteria decision-making (MCDM) problem by using the proposed operators within the circular environment. To validate the effectiveness and priority, this paper presents a numerical example of MCDM, and a comparison analysis was conducted to verify the practicality of the proposed approach.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A segmented differential evolution with enhanced diversity and semi-adaptive parameter control 一种具有增强多样性和半自适应参数控制的分段差分进化算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01883-z

Huarong Xu, Zhiyu Zhang, Qianwei Deng, Shengke Lin

{"title":"A segmented differential evolution with enhanced diversity and semi-adaptive parameter control","authors":"Huarong Xu, Zhiyu Zhang, Qianwei Deng, Shengke Lin","doi":"10.1007/s40747-025-01883-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01883-z","url":null,"abstract":"Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first, the algorithm is structured into three distinct stages, each employing a unique new mutation strategy and designed a new evolutionary scheme based on this segmentation, to better balance exploration and development at all stages of the process. Secondly, building on the idea of parameter restriction in the evolutionary stage of delineation, a semi-adaptive parameter control method based on the fitness of the irrelevant function is proposed which effectively solves the instability problem of excessive fluctuations in the convergence of adaptive parameters. Thirdly, new diversity maintenance mechanisms are proposed, including population initialization, shrinkage, and updating, which better ameliorated the conflicting issues of search range and search rate that existed at all stages of the DE variant. Finally, comprehensive experiments were conducted on the CEC2013, CEC2014, and CEC2017 benchmark test suites to rigorously assess the accuracy, convergence rate, and overall effectiveness of each module. The results show that MSA-DE exhibits strong competitiveness in single-objective optimisation problems. In addition, the experimental results demonstrate the superiority of the algorithm for real-world engineering problems.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0