Swarm and Evolutionary Computation最新文献

{"title":"Optimal financial portfolio selection using a metaheuristic approach with multiple strategies","authors":"Limin Wang ,&nbsp;Guosen Lin ,&nbsp;Qijun Zhang ,&nbsp;Muhammet Deveci ,&nbsp;Seifedine Kadry ,&nbsp;Mingyang Li","doi":"10.1016/j.swevo.2025.102162","DOIUrl":"10.1016/j.swevo.2025.102162","url":null,"abstract":"<div><div>Portfolio optimisation with cardinality constraints has been extensively studied in the realm of financial investment, recognised as an NP-hard quadratic programming problem. As an innovative metaheuristic approach, the dung beetle optimiser leverages its unique optimisation search mechanism to effectively tackle unconstrained optimisation problems. However, the realities of portfolio optimisation involve various constraints; thus, the original dung beetle optimiser may not suffice. Consequently, this study develops an improved dung beetle optimiser to address cardinality constrained portfolio optimisation, incorporating a new decision variable update strategy, a constraint handling strategy, and a local search strategy. These techniques facilitate the efficient selection of assets from among multiple candidate assets. To validate the capabilities of the indicated methodologies, five datasets from OR-Library and six datasets from NGINX are employed for testing. The results from these datasets consistently indicate that the proposed strategies outperform existing alternatives. Furthermore, the comparison results with various methods presented in other works demonstrate that the proposed technology is competitive in the realm of cardinality constrained portfolio optimisation.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102162"},"PeriodicalIF":8.5,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097054","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":"DA2MODE: Dynamic Archive with Adaptive Multi-Operator Differential Evolution for numerical optimization","authors":"Mohamed Reda ,&nbsp;Ahmed Onsy ,&nbsp;Amira Y. Haikal ,&nbsp;Ali Ghanbari","doi":"10.1016/j.swevo.2025.102130","DOIUrl":"10.1016/j.swevo.2025.102130","url":null,"abstract":"<div><div>This paper presents Dynamic Archive with Adaptive Multi-Operator Differential Evolution (DA2MODE), a new algorithm that aims to boost the performance of meta-heuristic and evolutionary methods in numerical optimization. DA2MODE introduces a Progressive Adaptive Selector with Exponential Smoothing (PASES), which dynamically updates the selection probabilities of both mutation and crossover operators. Unlike prior approaches that emphasize only mutation operators or rely on short-term success within the current generation, PASES adapts based on cumulative operator performance over time, thus favoring the best-performing operators more reliably. DA2MODE employs an Adaptive Non-Elite Archive Update (ANEAU) mechanism that injects a controlled fraction of non-elite solutions into the archive. ANEAU promotes early exploration, which is gradually reduced to strengthen exploitation. Additionally, the control parameters (crossover probability and mutation factor) are automatically tuned in DA2MODE, allowing full adaptivity of both operator selection and parameter control. Extensive experiments on the CEC2017/2018, CEC2020-2022, and 1000-dimensional CEC2013 benchmarks, along with four real-world engineering design problems, confirm that DA2MODE consistently outperforms 33 competitive algorithms, including CEC winners and recent advanced DE variants. It achieves top performance across all statistical tests, demonstrating superior convergence speed and final accuracy. These results establish DA2MODE as a robust, scalable, and reliable algorithm for solving complex numerical optimization problems. The source code of the DA2MODE algorithm is publicly available at: URL <span><span>https://github.com/MohamedRedaMu/DA2MODE-Algorithm</span><svg><path></path></svg></span> and URL <span><span>https://uk.mathworks.com/matlabcentral/fileexchange/182019-da2mode-algorithm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102130"},"PeriodicalIF":8.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097113","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":"Material delivery optimization for make-to-order reconfigurable job shops using an improved chaotic multi-verse algorithm","authors":"Qinge Xiao ,&nbsp;Kai Wang ,&nbsp;Chi Ma ,&nbsp;Ye Chen","doi":"10.1016/j.swevo.2025.102167","DOIUrl":"10.1016/j.swevo.2025.102167","url":null,"abstract":"<div><div>The increasing demand for product customization has highlighted the importance of make-to-order (MTO) material delivery. Although manufacturers have deployed intelligent reconfigurable job shops equipped with flexible workstations and automated guided vehicles (AGVs), challenges remain due to inefficient material scheduling, delayed deliveries, and the complexity arising from diverse material types. This study proposes an active delivery strategy based on a workshop material supermarket, in which both AGV path planning and workstation layout are jointly optimized in response to dynamically changing orders. A multi-objective delivery path model is formulated to support demand splitting while minimizing material delivery costs and maximizing timeliness satisfaction. The model incorporates constraints related to AGV capacity, path feasibility, and demand alignment. To address the nonlinearity and complexity of the problem, an improved chaotic multi-verse optimizer (ICMVO) is proposed. The algorithm employs chaotic encoding to enhance population diversity and mitigate premature convergence. It further integrates gravitational and collision operators to improve global and local search capabilities and adopts adaptive orbital dynamics control to balance exploration and exploitation. A dual-population iterative strategy is employed to enable joint decision-making on workstation coordinates, path direction, and vehicle assignment. Through comprehensive comparisons with state-of-the-art meta-heuristics, the superiority of the ICMVO algorithm and the effectiveness of its components are demonstrated. Moreover, the proposed material delivery optimization method is implemented in a cloud–edge–terminal system and validated in practical MTO reconfigurable job shops through improvements in productivity and cost efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102167"},"PeriodicalIF":8.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097053","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 preference modified inverted generational distance indicator guided algorithm for evolutionary multi-objective optimization","authors":"Fei Li ,&nbsp;Hao Tian ,&nbsp;Hao Shen ,&nbsp;Xingyi Zhang ,&nbsp;Jianchang Liu ,&nbsp;Zaiwu Gong","doi":"10.1016/j.swevo.2025.102169","DOIUrl":"10.1016/j.swevo.2025.102169","url":null,"abstract":"<div><div>Preference-based evolutionary multi-objective optimization algorithms have attracted much attention in the area of evolutionary computation. However, there are only a few researchers incorporating performance indicators for designing preference-based evolutionary algorithm. In this paper, we propose a preference modified inverted generational distance indicator guided algorithm, named PIGA, for evolutionary multi-objective optimization. The main purpose is that decision-makers provide their preferences, ultimately identifying the portion of Pareto optimal solutions where are located in region of interest. A new preference construction strategy based on coordinate transformation is first proposed. The reference points in the whole objective space can be projected into the preference space, obtaining the preferred reference points. The non-preferred reference points remain in the original objective space, outside the specified preference region. In addition, we define the distance between the candidate solution and preferred reference points as the preference distance and the distance to non-preferred reference points as the penalty distance. Finally, a preference-based modified inverted generational distance indicator is formulated to obtain the preferred optimal solutions according to the preferences and penalty distances. The comparative results are comprehensively analyzed by comparing it with some related preference-based evolutionary algorithms on some test instances. Experimental results have validated the effectiveness and feasibility of the proposed algorithm under different scenarios with the given preference range.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102169"},"PeriodicalIF":8.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097052","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 weight optimization algorithm based on decision variable grouping for large-scale multi-objective optimization problems","authors":"Hao Wang ,&nbsp;Shuwei Zhu ,&nbsp;Wei Fang ,&nbsp;Kalyanmoy Deb","doi":"10.1016/j.swevo.2025.102149","DOIUrl":"10.1016/j.swevo.2025.102149","url":null,"abstract":"<div><div>When solving large-scale multi-objective optimization problems (LSMOPs), the optimization effect of traditional multi-objective optimization algorithms deteriorates as the number of decision variables increases. The weight optimization method based on problem transformation can effectively address LSMOPs, demonstrating superior convergence compared to most evolutionary algorithms. However, existing problem transformation methods often fail to balance convergence and diversity, leading to get trapped in local optima. In order to effectively solve this problem, we propose an adaptive weight optimization algorithm based on variable grouping (GWOEA). The algorithm optimizes weights within groups to accelerate population convergence, while the adaptive control strategy boosts diversity, avoiding local optima and ensuring a balance between convergence and diversity during the optimization process. To reduce the size of solving LSMOPs, weight optimization is performed by grouping decision variables. The weights of variables within each group are first computed, and then these weights are directly optimized instead of the decision variables. The adaptive control strategy is designed to detect whether population evolution has stagnated and to handle stagnant populations, ensuring that the population retains its ability to explore. To evaluate the effectiveness of GWOEA, comprehensive comparative experiments are conducted on benchmark test problems, including variable sizes ranging from 500 to 5000. The results show that the proposed algorithm has relatively better optimization performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102149"},"PeriodicalIF":8.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097111","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 large-scale multi-objective optimization framework based on a dual-space attention mechanism","authors":"Xu Li ,&nbsp;Debao Chen ,&nbsp;Feng Zou ,&nbsp;Fangzhen Ge ,&nbsp;Zhenghua Xin","doi":"10.1016/j.swevo.2025.102089","DOIUrl":"10.1016/j.swevo.2025.102089","url":null,"abstract":"<div><div>Existing attention-based methods for large-scale multi-objective optimization (LMOAM) focus only on decision variables, using their variance to guide search behavior. However, single-space strategies ignore critical information in the objective space and the diversity and search efficiency are often degraded for solving multimodal multi-objective optimization problems (MOPs). To address this problem, a novel large-scale optimization framework that integrates a dual-space attention mechanism is proposed in this paper. Different from building attention only with information in decision space, a dual-space Key matrix that quantifies variable importance by combining decision-variable and objective-space distributions is first designed in the framework to refine the precision of the attention. Subsequently, a cross-space clustering method is adopted to select the representative solutions by analyzing the characteristics of individuals in both spaces to construct the Query matrix. The accuracy of attention allocation is improved. Finally, A linear inverse mapping strategy is used to enhance the diversity of the population by translating promising objective-space solutions back to the decision space. Unlike existing approaches, the characteristics of decision and objective space are linked with a new attention mechanism, and the exploration and exploitation of the population are well balanced. Three types of experiments are designed on two benchmark test sets with 500-dimensional and 1000-dimensional decision variables and the voltage transformer optimization problem to demonstrate the efficacy of the AIDF framework, experimental results indicate that AIDF surpasses comparative algorithms in terms of the average performance of IGD and HV.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102089"},"PeriodicalIF":8.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097110","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":"GTG-ACO: Graph Transformer Guided Ant Colony Optimization for learning heuristics and pheromone dynamics for combinatorial optimization","authors":"Abrar Rahman Abir ,&nbsp;Muhammad Ali Nayeem ,&nbsp;M. Sohel Rahman ,&nbsp;Md Adnan Arefeen","doi":"10.1016/j.swevo.2025.102147","DOIUrl":"10.1016/j.swevo.2025.102147","url":null,"abstract":"<div><div>Combinatorial optimization (CO) problems are fundamental to numerous real-world applications, ranging from logistics and scheduling to resource allocation. For solving CO problems, Ant Colony Optimization (ACO) is a widely used metaheuristic that simulates cooperative foraging behavior to iteratively construct high-quality solutions. However, traditional ACO suffers from handcrafted heuristic functions that fail to generalize across different instances and uniform pheromone initialization, which results in inefficient exploration and slow convergence.</div><div>To address these limitations, we introduce <strong>G</strong>raph <strong>T</strong>ransformer <strong>G</strong>uided <strong>A</strong>nt <strong>C</strong>olony <strong>O</strong>ptimization- <strong>GTG-ACO</strong>, a novel approach that jointly <em>learns</em> both heuristic and initial pheromone matrices, enabling the model to generalize across diverse problem instances without manual tuning. Additionally, GTG-ACO employs Graph Transformer augmented with Squeeze-and-Excitation (SE) network as the backbone for heuristic and pheromone learner. The Graph Transformers enable adaptive representation learning by leveraging attention mechanisms to dynamically capture structural relationships in graph representation of combinatorial optimization problems. Additionally, SE networks enhance the model by recalibrating feature importance, ensuring that critical information is amplified while suppressing less relevant features. Extensive evaluations on four combinatorial optimization problems—Traveling Salesman Problem (TSP), Capacitated Vehicle Routing Problem (CVRP), Single Machine Total Weighted Tardiness Problem (SMTWTP) and Bin Packing Problem (BPP)—demonstrate that GTG-ACO consistently outperforms state-of-the-art baselines achieving improvements ranging from 1% to 56%. Furthermore, we validate its real-world applicability by evaluating it on benchmark datasets TSPLIB and CVRPLIB. Thus, GTG-ACO establishes itself as a powerful and generalizable framework by jointly learning heuristic and pheromone matrices, enabling more informed exploration, which leads to superior solution quality in combinatorial optimization problems. Our code is publicly available at <span><span>https://github.com/abrarrahmanabir/GTG-ACO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102147"},"PeriodicalIF":8.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061046","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 evolutionary method with shift pattern learning for real-world multi-skilled personnel scheduling with flexible shifts","authors":"Ning Xue,&nbsp;Ruibin Bai,&nbsp;Huan Jin,&nbsp;Tianxiang Cui","doi":"10.1016/j.swevo.2025.102160","DOIUrl":"10.1016/j.swevo.2025.102160","url":null,"abstract":"<div><div>Personnel scheduling remains a significant organizational challenge with substantial potential for cost and time savings. Despite extensive research in this domain, few studies have been successfully implemented in practice, and even fewer have gained widespread acceptance among end-users. This gap between research and application often arises from oversimplified real-world models, which may result from subjective solution evaluations or a lack of collaboration between modelers and end-users. To bridge this gap, this paper proposes a machine learning-enhanced memetic algorithm (MLMA) that mimics schedules created by experts to solve a highly complex personnel scheduling problem involving multi-skilled workers and flexible shift types (irregular workforce)—a real-world challenge commonly faced in the hospitality sector. By leveraging historical scheduling preferences, the MLMA generates solutions that align with past practices, enhancing their practicality and appeal to end-users. Experiments conducted on real-life instances demonstrate the effectiveness of the proposed approach in addressing real-world problems, where the workforce is predominantly part-time, possesses mixed skills, and requires flexible shifts. Furthermore, the results highlight the MLMA’s ability to identify shift patterns that closely resemble historical schedules, underscoring its potential for practical implementation and its role in bridging the gap between research and real-world application.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102160"},"PeriodicalIF":8.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049197","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":"Hybrid metaheuristic algorithms for image watermarking: An experimental study","authors":"Anna Melman,&nbsp;Oleg Evsutin","doi":"10.1016/j.swevo.2025.102163","DOIUrl":"10.1016/j.swevo.2025.102163","url":null,"abstract":"<div><div>Invisible image watermarking is a promising method for protecting the copyright of digital images such as photographs, illustrations, and scans. An effective watermarking algorithm embeds a special mark into an image that does not change the image content but can be extracted from it even after some common post-processing operations such as cropping or compression. Many authors use metaheuristic optimization algorithms to achieve a trade-off between imperceptibility and robustness of embedding. In recent years, researchers have been interested in hybrid metaheuristics, which combine operations of individual metaheuristics in some way. However, designs and compositions of hybrid metaheuristic optimization schemes for image watermarking have not been sufficiently studied to date. In this paper, we present an experimental study of various hybrid metaheuristics including sequential, interleaved, and parallel schemes for popular bioinspired optimization algorithms including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, firefly algorithm, and artificial bee colony algorithm. We evaluate the effectiveness of hybrid metaheuristics for image watermarking using an algorithm based on changing the ratio between absolute values ​​of sums of discrete cosine transform coefficient groups as an example and perform an experimental comparison of different schemes. The results of the study show that a approach to metaheuristic hybridization and a composition of hybrid scheme significantly affect the imperceptibility and robustness of the image watermarking algorithm. In particular, the interleaved hybridization type provides the best results for the algorithm under consideration.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102163"},"PeriodicalIF":8.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049772","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 learning-and-knowledge-assisted multi-population collaborative evolutionary algorithm for integrated design-production-distribution scheduling problems in mass personalized customization","authors":"Yanhe Jia ,&nbsp;Wei Wang ,&nbsp;Jian Zhang","doi":"10.1016/j.swevo.2025.102158","DOIUrl":"10.1016/j.swevo.2025.102158","url":null,"abstract":"<div><div>Recently, new requirements are proposed for the manufacturing industry transitioning to distributed production models due to emergence of mass personalized customization. Integrated scheduling of design, production and distribution, mixed management of batch and flexible manufacturing are becoming the imminent challenges faced by enterprises. This article proposes an integrated design-production-distribution scheduling problem in distributed mixed shops. It considers distributed flow shops for batch manufacturing and distributed flexible job shops for flexible manufacturing. First, a mixed integer linear programming model is formulized to minimize the maximum completion time, total costs, and total tardiness. Second, a learning-and-knowledge-assisted multi-population collaborative evolutionary algorithm is developed to settle the model. Genetic operators are adopted to improve the global and local search abilities. Three subpopulations with adaptive crossover and mutation probabilities are constructed to enhance the convergence and diversity of population. A Q-learning-assisted cooperative approach is adopted to realize the information communication among subpopulations in the genetic operations. The Q-learning method is used to intelligently choose parent individuals from three subpopulations by utilizing its self-learning strategies. A variable neighborhood search approach considering problem-knowledge neighborhood structures is devised to refine the excellent individuals in population. Finally, the presented algorithm is compared against three well-known intelligent optimization methods on a collection of instances. Comparison outcomes verify the superiority of the developed algorithm in handling the considered problem.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102158"},"PeriodicalIF":8.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049200","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}