Swarm and Evolutionary Computation最新文献_第7页

Exploring module interactions in modular CMA-ES across problem classes 探索模块化CMA-ES中跨问题类的模块交互

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-14 DOI: 10.1016/j.swevo.2025.102116

Ana Nikolikj , Tome Eftimov

{"title":"Exploring module interactions in modular CMA-ES across problem classes","authors":"Ana Nikolikj , Tome Eftimov","doi":"10.1016/j.swevo.2025.102116","DOIUrl":"10.1016/j.swevo.2025.102116","url":null,"abstract":"<div><div>This study presents an in-depth analysis of module importance within the modular CMA-ES (modCMA-ES) algorithm using exploratory data analysis and large-scale benchmarking across the BBOB suite. Rather than introducing new algorithms, our contribution lies in uncovering how individual modules and their interactions influence optimization performance across diverse black-box problem classes. We evaluate 324 modCMA-ES variants across 24 problem classes using functional ANOVA (f-ANOVA) to quantify the variance in performance attributable to individual, pairwise, and triplet module interactions. Results reveal substantial variation in module importance across problem classes and highlight strong alignment between module interaction patterns and high-level landscape features, particularly multi-modality. Further, we demonstrate that configuring only the most important modules — identified via f-ANOVA — achieves performance comparable to or better than the single-best solver, especially in high-dimensional settings. This analysis, conducted at both low (5D) and high (30D) dimensions, offers actionable insights into module interactions within the mod-CMA-ES framework.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102116"},"PeriodicalIF":8.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841646","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}

引用次数: 0

Dimensional window method: A plug-in-style large-scale handling technique for evolutionary algorithm 维度窗口法：一种插件式的进化算法大规模处理技术

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-13 DOI: 10.1016/j.swevo.2025.102100

Yafeng Sun , Xingwang Wang , Junhong Huang , Bo Sun , Peng Liang

{"title":"Dimensional window method: A plug-in-style large-scale handling technique for evolutionary algorithm","authors":"Yafeng Sun , Xingwang Wang , Junhong Huang , Bo Sun , Peng Liang","doi":"10.1016/j.swevo.2025.102100","DOIUrl":"10.1016/j.swevo.2025.102100","url":null,"abstract":"<div><div>Large-scale optimization constitutes a pivotal characteristic of numerous real-world problems, where large-scale evolutionary algorithms emerge as a potent instrument for addressing such intricacies. However, existing methods are typically tailored to address only a particular class of problems and lack the versatility to be readily adapted to other evolutionary algorithms or generalized across diverse problem domains. To address the issue above, this paper proposes the dimensional window method, a simple yet effective enhancement that can be seamlessly integrated into low-dimensional evolutionary algorithms to bolster their performance in large-scale optimization. Specifically, the dimensional window method involves grouping a subset of randomly selected dimensions into a window during each iteration, restricting the population’s evolution to the dimensions within this window. Furthermore, the effectiveness of the dimensional window method is analyzed, and the window is improved based on the insights gained, including the isometric segmentation individual-level window length and the neural network-guided window element. Extensive experiments on single-objective, multi-objective, constrained multi-objective, and discrete test problems with large-scale attributes demonstrate that the proposed method significantly mitigates the curse of dimensionality and enhances the performance of evolutionary algorithms in large-scale settings. A more significant advantage lies in the fact that the proposed plug-ins not only demonstrate remarkable performance when tackling real-world challenges, such as ratio error estimation problems, but also offer easily integration into existing evolutionary algorithm platforms, all while being highly user-friendly for evolutionary algorithm users.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102100"},"PeriodicalIF":8.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828003","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}

引用次数: 0

A review of static and dynamic charging in electric vehicle routing: Transition, algorithms and future directions 电动汽车路径中静态充电与动态充电：过渡、算法与未来发展方向

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-13 DOI: 10.1016/j.swevo.2025.102105

Yunlong Wang , Minh Kieu , Avishai (Avi) Ceder , Prakash Ranjitkar

{"title":"A review of static and dynamic charging in electric vehicle routing: Transition, algorithms and future directions","authors":"Yunlong Wang , Minh Kieu , Avishai (Avi) Ceder , Prakash Ranjitkar","doi":"10.1016/j.swevo.2025.102105","DOIUrl":"10.1016/j.swevo.2025.102105","url":null,"abstract":"<div><div>Electric vehicles (EVs) have significant potential to reduce emissions and revolutionise transportation. This paper provides the first comprehensive review of Electric Vehicle Routing Problems (EVRPs) explicitly emphasising dynamic charging (DC-EVRP) methodologies, contrasting with conventional static charging (SC-EVRP). By systematically analysing 140 studies, we identify critical transitions from static to dynamic charging paradigms, highlighting dynamic wireless and overhead catenary systems that enable continuous vehicle operation, reduced battery capacity needs, and enhanced operational flexibility. From an algorithmic perspective, this review rigorously analyses heuristic, meta-heuristic, and deep reinforcement learning (DRL) methods employed across EVRP variants. While classical algorithms have been extensively developed for SC-EVRP, their adaptation to DC-EVRP remains limited, presenting unique challenges in real-time decision-making and energy management. Conversely, DRL and data-driven approaches show promise for integrating traffic, grid, and vehicle states, but remain under-explored in large-scale, dynamic charging contexts. To bridge existing research gaps, we propose future directions, including the design of resilient hybrid-charging strategies, the development of unified digital frameworks and benchmark datasets, and the advancement of transferable DRL/GNN models tailored to the complexities of DC-EVRP. By combining algorithmic innovation with integrated dynamic charging infrastructure, this review outlines a pathway for advancing EVRP solutions towards scalable, sustainable, and system-wide intelligent electric mobility.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102105"},"PeriodicalIF":8.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828004","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}

引用次数: 0

Automatic Entity Matcher Combination for Heterogeneous Entity Alignment via Multi-chromosome Genetic Algorithm 基于多染色体遗传算法的异构实体匹配自动组合

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-13 DOI: 10.1016/j.swevo.2025.102117

Chengsheng Chi , Xingsi Xue , Pei-Wei Tsai , Himanshu Dhumras , Guojun Mao

{"title":"Automatic Entity Matcher Combination for Heterogeneous Entity Alignment via Multi-chromosome Genetic Algorithm","authors":"Chengsheng Chi , Xingsi Xue , Pei-Wei Tsai , Himanshu Dhumras , Guojun Mao","doi":"10.1016/j.swevo.2025.102117","DOIUrl":"10.1016/j.swevo.2025.102117","url":null,"abstract":"<div><div>Ontology Matching (OM) plays a critical role in enabling seamless data interoperability and automated reasoning by discovering semantic correspondences between heterogeneous ontologies. Because no single Entity Matcher (EM) can fully capture lexical, structural, and semantic variations among diverse ontologies, aggregating multiple EMs is essential to achieve accurate matching results. To enhance the effectiveness and efficiency of OM, we propose a novel Multi-chromosome Genetic Algorithm (MGA), which includes three new components. First, a new multi-chromosome encoding mechanism is designed to simultaneously optimize aggregation weights and thresholds, thereby enhancing matching accuracy. Second, to improve search efficiency, a novel breeding crossover operator is developed to capture complex interrelations among multiple chromosomes within an individual. Finally, a multi-chromosome local search strategy is presented to refine elite solutions to further boost optimization performance. Extensive experiments conducted on the Ontology Alignment Evaluation Initiative (OAEI) Benchmark and Conference datasets demonstrate that MGA consistently outperforms state-of-the-art methods. Specifically, MGA achieves substantial improvements in f-measure scores, achieving an average f-measure of 0.87 on the Benchmark dataset and 0.70 on the Conference dataset, and outperforming the best matching methods by 2.5% and 3.8%, respectively, which confirms its robustness and effectiveness in matching heterogeneous ontologies.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102117"},"PeriodicalIF":8.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828005","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}

引用次数: 0

Dynamic integrated optimization of batching and routing in narrow-aisle order picking systems with congestion consideration 考虑拥塞的窄通道拣货系统中分批和路由的动态集成优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-06 DOI: 10.1016/j.swevo.2025.102109

Yanghua Pan , Shanshan Li , Ting Qu , Liqiang Ding , Naiqi Wu , George Q. Huang

{"title":"Dynamic integrated optimization of batching and routing in narrow-aisle order picking systems with congestion consideration","authors":"Yanghua Pan , Shanshan Li , Ting Qu , Liqiang Ding , Naiqi Wu , George Q. Huang","doi":"10.1016/j.swevo.2025.102109","DOIUrl":"10.1016/j.swevo.2025.102109","url":null,"abstract":"<div><div>The e-commerce industry has driven logistics centers to process massive and time sensitive orders more efficiently and effectively. However, the high land and construction costs have forced the warehouse to adopt a narrow channel layout to increase its capacity, which in turn has caused congestion and further reduced its operational efficiency. Therefore, how to carry out optimization of order batching and picking paths in narrow channels to avoid channel congestion and pursue a lowest total cost has become the problem that this article aims to solve. This article develops a Markov decision process model to address the online order multi-period picking planning optimization challenge, and proposes the Genetic Algorithm-Ratliff Rosenthal-Time Weighted Similarity (GA-RR-MTWS) algorithm which innovatively integrates congestion considerations into the optimization process. A myopic cost function approximation strategy is introduced aiming at minimizing the total cost of a whole working day. Comparative experimental analysis demonstrates the modified cost function GA-RR-MTWS's superior performance in reducing total picking cost and congestion, particularly in complex, multi-aisle environments with multiple pickers. The method's ability to manage congestion and optimize routing significantly improves overall warehouse efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102109"},"PeriodicalIF":8.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781509","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}

引用次数: 0

Dynamic flexible job shop scheduling algorithm for multi-task collaborative optimization 多任务协同优化的动态柔性作业车间调度算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-06 DOI: 10.1016/j.swevo.2025.102114

Zeyin Guo , Lixin Wei , Xin Li , Rui Fan

{"title":"Dynamic flexible job shop scheduling algorithm for multi-task collaborative optimization","authors":"Zeyin Guo , Lixin Wei , Xin Li , Rui Fan","doi":"10.1016/j.swevo.2025.102114","DOIUrl":"10.1016/j.swevo.2025.102114","url":null,"abstract":"<div><div>Due to customer demand or market changes, production orders in intelligent manufacturing workshops become uncertain. Based on the above issues, an order detection framework is constructed to detect different types of order changes. Different dynamic response mechanisms are designed for different types of order changes. The scheduling of jobs in discrete manufacturing has composability, resulting in a huge search space. Previous research methods that used a single population to solve scheduling schemes could not fully explore the search space. Considering the characteristics of multitasking in job shop scheduling, this study designs an auxiliary task collaborative optimization algorithm (ATCOA) to solve the optimal rescheduling schemes. To escape from the situation of optimizing local optima in the main task, a knowledge transfer probability model based on the main task is adopted to determine population communication between tasks. A multitask knowledge transfer strategy is proposed for exchanging individual information between tasks to improve the diversity distribution ability of optimization algorithm. To evaluate the effectiveness of the ATCOA algorithm, it is compared with other algorithms on the constructed dynamic order test cases. In the case of order cancellation and insertion, ATCOA obtained 10 minimum inverted generation distance and maximum spread metric values and 9 hypervolume values, respectively. ATCOA has improved completion efficiency by 8.9% compared to scheduling rules. In engineering simulation cases, the ATCOA algorithm improved workload deviation by 41.9% compared to other algorithms. The experimental results show that the ATCOA algorithm is more efficient and stable than other algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102114"},"PeriodicalIF":8.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781510","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}

引用次数: 0

Elite knowledge transfer within lower-level searches for bilevel optimization 精英知识在低层次搜索中转移，以实现双层优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-06 DOI: 10.1016/j.swevo.2025.102097

Yutao Lai, Hai-Lin Liu, Yukai Xu, Lei Chen

{"title":"Elite knowledge transfer within lower-level searches for bilevel optimization","authors":"Yutao Lai, Hai-Lin Liu, Yukai Xu, Lei Chen","doi":"10.1016/j.swevo.2025.102097","DOIUrl":"10.1016/j.swevo.2025.102097","url":null,"abstract":"<div><div>Bilevel optimization problems pose significant challenges for evolutionary algorithms (EAs) due to their nested structure. This paper introduces an efficient evolutionary bilevel algorithm that leverages elite knowledge transfer to tackle these challenges. Firstly, this paper employs a biobjective source selection strategy to balance convergence quality with relevance to the target lower-level problem. Building on this, a multi-source elite knowledge transfer mechanism constructs an elite Gaussian distribution model from source lower-level solutions, facilitating efficient parameterized knowledge transfer to accelerate the optimization of the target lower-level problem. Additionally, an adaptive strategy for reducing the lower-level population size further enhances algorithmic efficiency. Evaluated on benchmark test suites and real-world problems, the proposed algorithm demonstrates superior efficiency and accuracy compared to state-of-the-art bilevel optimization algorithms, underscoring the effectiveness of the elite knowledge transfer and adaptive reduction strategies. The source code for EKTBO has been publicly released at the following link: <span><span>https://github.com/tg980515/EKTBO</span><svg><path></path></svg></span></div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102097"},"PeriodicalIF":8.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781506","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}

引用次数: 0

Knowledge-assisted evolutionary task scheduling for hierarchical multiagent systems with transferable surrogates 具有可转移代理的分层多智能体系统的知识辅助进化任务调度

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-06 DOI: 10.1016/j.swevo.2025.102107

Tonghao Wang , Xingguang Peng , Xiaokang Lei , Handing Wang , Yaochu Jin

{"title":"Knowledge-assisted evolutionary task scheduling for hierarchical multiagent systems with transferable surrogates","authors":"Tonghao Wang , Xingguang Peng , Xiaokang Lei , Handing Wang , Yaochu Jin","doi":"10.1016/j.swevo.2025.102107","DOIUrl":"10.1016/j.swevo.2025.102107","url":null,"abstract":"<div><div>Task scheduling is a primary step of a hierarchical multiagent system (HMAS) before solving tasks, presenting significant challenges due to its NP-hard complexity and variable-size decision space with different numbers of decision variables. This variability arises because a key decision is determining the number of agents to deploy, which directly affects the dimension of the decision vector. Evolutionary algorithms (EAs) have been widely adopted in addressing the task scheduling problem for HMAS due to their ability to solve NP-hard problems. However, applying conventional fixed-length EAs to such problems often necessitates techniques like expanding the decision space, which negatively impacts search efficiency. Meanwhile, the evaluations of the candidate solutions need physics-based simulations with complex dynamics, which require high computational costs. To solve the HMAS task scheduling problem efficiently, our approach leverages domain knowledge by a genetic programming framework alongside a knowledge-data dual-driven surrogate, which avoids searching in expanded decision spaces and facilitates low-cost evaluation. Notably, the proposed surrogate model can be easily transferred among different task settings, further decreasing the computational load in deploying the HMAS in real-world applications. The effectiveness of the proposed algorithm is validated through extensive simulations on an unmanned ground vehicle/unmanned aerial vehicle (UGV/UAV) cooperation system, showcasing superior efficiency and efficacy. Moreover, the proposed algorithm is also validated in a real-world multi-robot system, further demonstrating the efficacy and efficiency of the method, as well as the transferability of the proposed surrogate model.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102107"},"PeriodicalIF":8.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781505","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}

引用次数: 0

An adaptive response algorithm based on dual-space detection for dynamic multiobjective optimization 基于双空间检测的动态多目标优化自适应响应算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-05 DOI: 10.1016/j.swevo.2025.102092

Xiaozhong Yu, Jinhua Zheng, Yaru Hu, Junwei Ou, Juan Zou

{"title":"An adaptive response algorithm based on dual-space detection for dynamic multiobjective optimization","authors":"Xiaozhong Yu, Jinhua Zheng, Yaru Hu, Junwei Ou, Juan Zou","doi":"10.1016/j.swevo.2025.102092","DOIUrl":"10.1016/j.swevo.2025.102092","url":null,"abstract":"<div><div>Efficiently tracking the dynamically changing Pareto-optimal set (POS) or Pareto-optimal front (POF) is a core task in dynamic multiobjective optimization. Most dynamic multi-objective evolutionary algorithms (DMOEAs) implement dedicated response mechanisms to mitigate the impact of environmental changes. To address the critical yet underexplored impact of varying change severities in both the POS and the POF, we propose an adaptive response algorithm based on dual-space detection, named ARA-DMOEA. Our approach incorporates a dual-space change severity detection mechanism that quantifies POS and POF variations, dynamically classifying changes as minor or significant. Based on this real-time assessment, ARA-DMOEA adaptively activates tailored response strategies. Specifically, when significant changes are detected in either space, a Gated Recurrent Unit (GRU) prediction model generates high-quality initial populations by leveraging historical solution patterns. For minor POS changes, a Centroid-guided Differential Prediction (CDP) strategy exploits population shift trends to maintain solution diversity. For minor POF changes, a Random Solution Generation (RSG) strategy enhances diversity by expanding sampling ranges around predicted ideal and nadir points. By synergistically combining these strategies according to dual-space severity detection, ARA-DMOEA dynamically optimizes its response to environmental shifts. In comparison with six state-of-the-art algorithms on a series of dynamic multiobjective problems, ARA-DMOEA demonstrates superior adaptability to environmental changes while achieving better convergence and diversity.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102092"},"PeriodicalIF":8.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781508","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}

引用次数: 0

Heterogeneous sensor integration through Co-Evolutionary Genetic Programming with Multiple Individual Representations 基于多个体表示的协同进化遗传规划的异构传感器集成

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-05 DOI: 10.1016/j.swevo.2025.102098

Xingsi Xue , Jerry Chun-Wei Lin

{"title":"Heterogeneous sensor integration through Co-Evolutionary Genetic Programming with Multiple Individual Representations","authors":"Xingsi Xue , Jerry Chun-Wei Lin","doi":"10.1016/j.swevo.2025.102098","DOIUrl":"10.1016/j.swevo.2025.102098","url":null,"abstract":"<div><div>Efficient integration of heterogeneous sensors is essential for optimizing complex sensor systems, however, variations in sensor types, data formats, and measurement units create significant challenges for seamless data integration and interoperability. Similarity Features (SFs) are commonly used to quantify relationships across diverse sensor data, yet no single SF is universally effective due to the inherent complexity of sensor data. Thus, combining multiple SFs is necessary for accurate integration. Although Genetic Programming (GP) offers a powerful solution for constructing SF combinations, it often encounters difficulties with the complex interactions in heterogeneous data, leading to local optima. To overcome these challenges, this paper introduces Co-Evolutionary Genetic Programming with Multiple Individual Representations (CEGP-MIR), which combines tree-based and linear GP representations to enhance the search and optimization process through dynamic population interaction. This approach includes a novel interaction mechanism for adaptive cooperation between representations and new crossover operators to prevent stagnation in local optima. Experiments use OAEI’s Benchmark, Conference datasets and ten real-world sensor datasets to test the performance of CEGP-MIR. Results demonstrate that the designed CEGP-MIR enhances sensor entity alignment and improves overall efficiency in heterogeneous sensor data integration.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102098"},"PeriodicalIF":8.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771535","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}

引用次数: 0