Swarm and Evolutionary Computation最新文献

{"title":"A knowledge transfer-based strategy for constrained multiobjective optimization","authors":"Shaoning Liu ,&nbsp;Jian Feng ,&nbsp;Shengxiang Yang ,&nbsp;Jun Zheng ,&nbsp;Yu Yao","doi":"10.1016/j.swevo.2025.102111","DOIUrl":"10.1016/j.swevo.2025.102111","url":null,"abstract":"<div><div>The complex constraints in constrained multiobjective optimization problems may cause the Pareto front to be distributed on disconnected feasible boundaries. Most existing evolutionary algorithms encounter challenges in obtaining the entire Pareto front due to inappropriate cooperation between the populations. The ideology of knowledge transfer provides inspiration for addressing complex optimization problems. Based on this, this paper proposes a knowledge transfer-based coevolutionary algorithm, which adopts the idea of divide-and-conquer and two combined into one. The algorithm derives the original constrained multiobjective optimization problem into two problems, both of which share the same optimization objective but follow distinct search trajectories. Specifically, one problem focuses on global search, while the other emphasizes local search. A knowledge transfer strategy is proposed to achieve the exchange of complementary information between these two problems in the evolutionary directions. This strategy assists in solving the derived problem by transferring promising individuals that remain undiscovered in the search trajectories. The optimal solution of the original constrained multiobjective optimization problem is obtained. Experiments conducted on 56 benchmark problems show superior or competitive performance compared with 11 state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102111"},"PeriodicalIF":8.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916297","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":"Discrete optimization algorithms for distributed bi-agent flowshop scheduling with release dates","authors":"Danyu Bai ,&nbsp;Wenjia Zheng ,&nbsp;Chenbo Zang ,&nbsp;Jie Yang ,&nbsp;Chin-Chia Wu ,&nbsp;Hu Qin","doi":"10.1016/j.swevo.2025.102101","DOIUrl":"10.1016/j.swevo.2025.102101","url":null,"abstract":"<div><div>The globalization of production has accelerated the growth of contract manufacturing, as brand firms increasingly outsource production to specialized manufacturers to reduce costs and improve efficiency. To meet rising production demands, contract manufacturers establish production facilities across global regions, leveraging localized advantages in labor costs, raw material access, and logistics infrastructure. Contract manufacturers in distributed assembly-line systems face the critical challenge of dynamically coordinating order allocation across decentralized facilities to satisfy multi-client requirements. This study introduces a distributed bi-agent permutation flowshop scheduling for minimizing the makespans of both agents while considering release dates to simulate real-world production scenarios. An exact branch-and-bound algorithm is proposed for optimizing the weighted sum of two objectives. A novel Q-learning-based artificial bee colony algorithm is presented to construct high-quality Pareto frontiers for the bi-objective optimization problem. The effectiveness of the proposed algorithms is validated through a comprehensive set of numerical experiments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102101"},"PeriodicalIF":8.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913058","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":"Differential evolution algorithm with local and global parameter adaptation","authors":"Xiao Lin Jin ,&nbsp;Sheng Xin Zhang ,&nbsp;Li Ming Zheng ,&nbsp;Shao Yong Zheng","doi":"10.1016/j.swevo.2025.102125","DOIUrl":"10.1016/j.swevo.2025.102125","url":null,"abstract":"<div><div>Differential Evolution (DE) is an effective meta-heuristic algorithm for numerical optimization. However, it suffers from persistent limitations such as sensitivity to parameter settings and premature convergence tendencies. This paper presents a novel Local and Global Parameter Adaptation (LGP) mechanism to mitigate these deficiencies through two key innovations. First, we develop a dual historical memory strategy that dynamically classifies successful control parameters into local or global historical record based on the Euclidean distance between parent-offspring vector pairs, the local and global historical memory are updated accordingly at each generation. Second, we introduce a parameter adaptation strategy that adaptively selects elements from appropriate historical memory for the generation of new control parameters to maintain exploitation-exploration balance. Extensive experimental validation demonstrates LGP’s effectiveness. When integrated with four DE variants, LGP consistently improves their performance, and the LGP-enhanced algorithm demonstrates remarkable performance compared with seven State-of-the-Art DE algorithms. Results confirm that LGP improves solution accuracy and prevents premature convergence simultaneously.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102125"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913057","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":"Metaheuristic-optimized TabNet ensemble for accurate and interpretable obesity classification","authors":"Zarindokht Helforoush,&nbsp;Mitra Shojaie,&nbsp;Sahel Arghamiri","doi":"10.1016/j.swevo.2025.102128","DOIUrl":"10.1016/j.swevo.2025.102128","url":null,"abstract":"<div><div>Obesity is a complex global health issue with severe implications for both individual well-being and public health systems. It has been traditionally challenging to predict and diagnose due to its multifactorial nature, involving genetic, behavioral, and environmental factors. While classical regression models have been extensively used for obesity prediction, their limitations have prompted the exploration of more advanced methodologies. In this study, we leverage Deep Learning (DL) techniques, particularly TabNet, to address the challenges of obesity classification in tabular data—a domain where DL’s potential has often been underutilized. Our approach enhances the TabNet architecture through effective hyperparameter tuning, utilizing Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Hunger Games Search (HGS). The resulting models, TabNet-PSO, TabNet-GWO, and TabNet-HGS are combined into a novel ensemble that demonstrates superior performance in obesity classification compared to conventional machine-learning models and recent studies. Additionally, Explainable Artificial Intelligence techniques are employed to provide both local and global interpretability of model predictions, using SHapley Additive exPlanations (SHAP). This interpretability is crucial in clinical settings, where understanding the underlying factors influencing predictions is essential. The study’s findings offer significant contributions to the early detection and management of obesity, providing healthcare professionals with precise and interpretable predictions to guide intervention strategies.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102128"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913056","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":"Incremental learning-enhanced ensemble surrogate-assisted evolutionary algorithm for lifelong berth allocation and quay crane assignment problems","authors":"Li-Sha Xu ,&nbsp;Yi-Ming Wang ,&nbsp;Ting Huang ,&nbsp;Yue-Jiao Gong ,&nbsp;Jing Liu","doi":"10.1016/j.swevo.2025.102133","DOIUrl":"10.1016/j.swevo.2025.102133","url":null,"abstract":"<div><div>The berth allocation and quay crane assignment problem (BACAP) is a critical challenge in maritime transport, especially in lifelong scenarios that are rarely addressed in the current literature but essential for practical applications. The Lifelong BACAP (LBACAP) presents new challenges, such as the uncertain arrival of vessels, limited resources, and inter-dependencies between vessels. To address these challenges, we propose an incremental learning-enhanced ensemble surrogate-assisted evolutionary algorithm, named IL-ESAEA, with three core designs. (1) The adaptive rolling-horizon strategy divides the LBACAP into consecutive time windows, each corresponding to an interconnected sub-LBACAP. (2) The ensemble surrogate-assisted evolutionary algorithm (ESAEA) approximates the computationally intensive and intricately designed decoding method for optimization, reducing computational costs while maintaining robust search capabilities for solving various BACAPs. (3) The incremental learning mechanism identifies connections between sub-LBACAPs in successive time windows, utilizing historical decisions to guide the optimization effectively. Experimental results demonstrate that IL-ESAEA consistently outperforms state-of-the-art algorithms and provides superior solutions with increased computational efficiency over time. This highlights the strong competitive edge of IL-ESAEA in solving LBACAPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102133"},"PeriodicalIF":8.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913055","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-pass planning for multi-vehicle cooperative urban demining: A knowledge-driven evolutionary approach with RL-enhanced neighborhood search","authors":"Hao Cheng ,&nbsp;Jin Yi ,&nbsp;Huayan Pu ,&nbsp;Jun Luo ,&nbsp;Chao Lu","doi":"10.1016/j.swevo.2025.102129","DOIUrl":"10.1016/j.swevo.2025.102129","url":null,"abstract":"<div><div>It has become increasingly urgent and necessary to coordinate multiple unmanned systems to efficiently execute a variety of complex tasks in place of humans. This paper focus on the problem of multi-vehicle demining in urban road networks (MVDMP). First, a mixed-integer programming model is established, taking into account both the topological connectivity of the road network and the demining width of the vehicles. Second, an evolutionary learning algorithm incorporating Q-learning (QEA) is proposed to effectively solve this problem. In the initialization phase, a hybrid initialization strategy, which includes two heuristic rules, is introduced to generate high-quality initial solutions. During the local search phase, six neighborhood search operators are proposed based on problem characteristics, and Q-learning is used to adaptively customize perturbation schemes for individuals. Additionally, the Metropolis acceptance criterion is employed to balance exploration and exploitation. Finally, extensive experiments on instances of varying sizes derived from urban road networks (Sioux Falls, Sydney, etc.) demonstrate the efficiency and superiority of the proposed method compared to other four state-of-the-art approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102129"},"PeriodicalIF":8.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902325","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":"A comprehensive survey of adaptive strategies in differential evolutionary algorithms","authors":"Xinggui Ye ,&nbsp;Jianping Li ,&nbsp;Peng Wang ,&nbsp;Ponnuthurai Nagaratnam Suganthan","doi":"10.1016/j.swevo.2025.102081","DOIUrl":"10.1016/j.swevo.2025.102081","url":null,"abstract":"<div><div>Classical differential evolution (DE) encounters premature convergence when dealing with diverse optimization problems. This challenge has encouraged extensive research efforts aimed at improving and enhancing the original methodologies. Among the various improvement techniques, adaptive strategies have been universally employed. However, there is a lack of systematic research on the adaptation mechanisms. This work comprehensively investigates the adaptive strategies adopted in DE algorithms. Typical adaptation strategies employed in DE algorithms are refined and summarized, highlighting their characteristics. A new taxonomy of adaptation strategies is proposed, categorizing them based on their primary properties, which include adaptations of control parameters, mutation strategies, population size, search space, learning schemes, and composite adaptations. The advantages and disadvantages of these adaptation strategies are summarized, elucidating their unique characteristics. Additionally, a general framework with an adaptive updating engine is proposed, which can serve as a reference for developing new DE algorithms or improving existing ones. The paper also highlights the challenges and open issues of adaptive strategies, suggesting several promising research directions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102081"},"PeriodicalIF":8.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893288","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":"Secure key based cloud security utilizing three-way protection with martino homomorphic encryption for preventing unauthorized data access","authors":"Ganji Ramanjaiah ,&nbsp;Tummala Srinivasa Ravi Kiran ,&nbsp;Ampalam Srisaila ,&nbsp;Annemneedi Lakshmanarao ,&nbsp;Komanduri Venkata Sesha Sai Ramakrishna ,&nbsp;Katakam Venkateswara Rao","doi":"10.1016/j.swevo.2025.102131","DOIUrl":"10.1016/j.swevo.2025.102131","url":null,"abstract":"<div><div>Cloud computing has transformed data storage and access by providing scalable and on-demand services. Nevertheless, it remains a priority issue to ensure the protection of sensitive data in cloud environments. Several existing security methods has fundamental shortcomings like poor threat prediction features, a failure to process encrypted data securely and high encryption time. To overcome these issues, this study proposes a novel secure key based cloud security utilizing Three-Way Protection with Martino Homomorphic Encryption for preventing unauthorized data access (SKCS-TWP-MHE-PUDA). Initially, the data are collected from Enron Email dataset. Then the input data is given to Reverse Lognormal Kalman Filter (RLKF) for data cleaning and normalization. Next, Koopman Theory Graph Convolutional Network (KTGCN) is used to analyze packet status, predict potential threats and prevent unauthorized cloud access. This real-time intrusion detection mechanism enables early anticipation of malicious activity. Meanwhile, Martino Homomorphic Encryption (MHE) is used to ensure data confidentiality by encrypting cloud-stored data such that only legitimate users decrypt and access it. The three-way security mechanism comprising user registration, intrusion detection and intrusion prevention strengthens overall protection. The performance of the proposed SKCS-TWP-MHE-PUDA method provides 26.68%, 25.75%, and 26.16% higher accuracy 29.08%, 30.70% and 16.26% higher precision when compared with existing techniques: Stochastic Gradient Descent long short-term memory dependent secure encryption approach for cloud data storage and retrieval in cloud computing environs (SGDLSTM-CDS-CCE), Blockchain Key Management: A Solution for Cloud Data Security (AES-BKY-CDS) and deep learning method with cryptographic transformation for enhancing data security in cloud environs (SqueezeNet-DS-CE) respectively.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102131"},"PeriodicalIF":8.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893289","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":"Multitree genetic programming with spherical-based operators for synthetic minority over-sampling technique in unbalanced data","authors":"Yuyang Cui ,&nbsp;Ziliang Du ,&nbsp;Hongwei Ge ,&nbsp;Guangyu Zou ,&nbsp;Yaqing Hou","doi":"10.1016/j.swevo.2025.102126","DOIUrl":"10.1016/j.swevo.2025.102126","url":null,"abstract":"<div><div>Unbalanced classification is a critical challenge in machine learning, with broad applications in real-world scenarios. Recent studies have emphasized the potential of Evolutionary Computation (EC)-based approaches, particularly Genetic Programming (GP), as an effective sampling strategy for addressing class imbalance. In contrast to traditional oversampling methods that rely on neighborhood information and predefined structures, GP autonomously selects high-quality instances and evolves structures to generate new ones. However, existing GP-based approaches primarily focus on undersampling, with limited exploration of instance generation. Additionally, the traditional Single-Tree Genetic Programming (STGP) structure struggles to adapt to tasks requiring the generation of multiple candidate datasets. To address these challenges, this paper introduces MTGP-SMOTE, a novel oversampling method based on Multi-Tree Genetic Programming (MTGP). Unlike STGP, which evolves a single tree per individual, MTGP evolves multiple trees within an individual, enabling the generation of diverse new instances while evolving as a complete dataset. The method also incorporates innovative MTGP crossover and mutation operators, designed to enhance exploration by focusing on trees beyond the hemispheres of the target minority class while preserving high-quality individuals throughout the evolutionary process. Experiments on 20 unbalanced datasets demonstrate that MTGP-SMOTE significantly outperforms traditional sampling methods in reducing classifier bias and improving classification accuracy. These results underscore MTGP-SMOTE as a powerful and effective solution for addressing unbalanced classification in machine learning.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102126"},"PeriodicalIF":8.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890848","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":"Dual-metric guided multi-strategy hybrid optimization for feature selection on high-dimensional medical data","authors":"Yan Kang ,&nbsp;Dongsheng Zheng ,&nbsp;Haining Wang ,&nbsp;Yue Peng ,&nbsp;Shixuan Zhou","doi":"10.1016/j.swevo.2025.102118","DOIUrl":"10.1016/j.swevo.2025.102118","url":null,"abstract":"<div><div>The high-dimensional feature selection (FS) problem is challenging in medical fields due to the “curse of dimensionality” and the intricate relationships among various features. Although hybrid FS methods achieve high-performance solutions according to various mutual information metrics, such as symmetrical uncertainty (SU) and maximal information coefficient (MIC), they often overlook the differences between these metrics, and are still need to improve search strategies to escape from local optima. To address these challenges, we propose a dual-metric guided multi-strategy hybrid FS method (DGM) for high-dimensional medical datasets. The importance of features are first evaluated based on the SU and MIC metrics, and then the redundancy between features are reduced by fast clustering and grouping strategies. Furthermore, a two-level sampling strategy is proposed to guarantee the diversity and complementarity of population by considering the Jaccard Similarity and the correlation between features. A novel set-based multi-population PSO is designed to collaboratively search the optimal feature subset while obtaining feature importance during the evaluation process by a tri-archive assisted evolution approach. Specifically, two local archives help individuals escape from local optima, while the global archive optimizes the population. Finally, we develop various squeeze-expand mechanisms to dynamically adjust both the search space and the length of individuals to effectively balance exploration and exploitation. The experimental results on 13 medical datasets show that DGM significantly improves classification performance while selecting fewer features. The T-test results further indicate that DGM significantly outperforms all comparison methods in classification performance on 10 datasets, highlighting its strong competitiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102118"},"PeriodicalIF":8.5,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885694","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}