Swarm and Evolutionary Computation最新文献

{"title":"Distributed heterogeneous flexible job-shop scheduling problem considering automated guided vehicle transportation via improved deep Q network","authors":"Minghai Yuan,&nbsp;Songwei Lu,&nbsp;Liang Zheng,&nbsp;Qi Yu,&nbsp;Fengque Pei,&nbsp;Wenbin Gu","doi":"10.1016/j.swevo.2025.101902","DOIUrl":"10.1016/j.swevo.2025.101902","url":null,"abstract":"<div><div>Distributed manufacturing has become a research hotspot in the context of economic globalization. The distributed heterogeneous flexible job-shop scheduling problem considering automated guided vehicle transportation (DHFJSP-AGV) extends the classic flexible job-shop scheduling problem (FJSP) but remains underexplored. DHFJSP-AGV involves four subproblems: assigning jobs to heterogeneous factories, scheduling jobs to machines, sequencing operations on machines and transporting jobs between machines using AGVs. Due to its complexity, this study proposes an improved deep Q network (DQN) real-time scheduling method aimed at minimizing makespan. A mixed integer linear programming model (MILP) of DHFJSP-AGV is developed and transformed into a Markov decision process (MDP). Eight general state features are extracted and normalized to represent the state space, while appropriate combination dispatching rules are selected as the action space. The state features of each scheduling point are input to the DQN, determining the factory, job, machine, and AGV for each process. Additionally, double DQN and an improved ε-greedy exploration are used to enhance the DQN. Numerical comparison experiments under different production configurations and real-world application in distributed flexible job-shop with dynamic map environment demonstrate the effectiveness and generalization capabilities of improved DQN.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101902"},"PeriodicalIF":8.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593066","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 heuristic distributed and no-wait method for solving multiagent task allocation problems with coupled temporal constraints","authors":"Wei Cui ,&nbsp;Yanxiang Feng ,&nbsp;Ye Cao ,&nbsp;Xiaoling Li ,&nbsp;Yikang Yang","doi":"10.1016/j.swevo.2025.101898","DOIUrl":"10.1016/j.swevo.2025.101898","url":null,"abstract":"<div><div>Temporal constraints, primarily arising from engagement rules and requiring tasks to be performed in a specific order, are critical in task allocation problems (TAPs). However, existing allocation methods often fall short of handling temporal constraints. This paper proposes a heuristic distributed and no-wait algorithm, called the Temporal-Constraints Performance Impact (TC-PI) algorithm, for solving multi-agent TAPs with temporal constraints. By requiring each agent either travels to or immediately executes its assigned task, the TC-PI eliminates unnecessary waiting time and effectively reduces the <em>average task completion time</em>. The proposed algorithm consists of three phases. Firstly, each agent sequentially adds tasks to its task list while ensuring temporal constraints are satisfied. Secondly, conflicts where multiple agents select the same task are resolved through local communication. Finally, any remaining conflicts caused by temporal constraints are further addressed. To maintain task order and minimize completion time, task significance is redefined by incorporating temporal relationships among tasks. A penalty mechanism prevents infinite task reallocation cycles, enhancing system robustness and avoiding deadlocks. Simulation results demonstrate that TC-PI effectively resolves temporal conflicts, achieves no-wait task allocations, and flexibly handles dynamic task arrivals.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101898"},"PeriodicalIF":8.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic multi-objective evolutionary algorithm based on dual-layer collaborative prediction under multiple perspective","authors":"Yaru Hu ,&nbsp;Yana Li ,&nbsp;Junwei Ou ,&nbsp;Jiankang Peng ,&nbsp;Jun Li ,&nbsp;Jinhua Zheng","doi":"10.1016/j.swevo.2025.101876","DOIUrl":"10.1016/j.swevo.2025.101876","url":null,"abstract":"<div><div>Prediction-based strategies become increasingly prominent in addressing dynamic multi-objective optimization problems (DMOPs). However, challenges remain in selecting predictive models and effectively utilizing historical solutions. In this paper, we propose a multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective and realized through the collaboration of three sub-strategies. From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time. Building on these global insights, the second layer employs a knee-point interval partitioning strategy that combines vector partitioning with knee-point-based predictions. This layer provides localized insights that complement the broader movement trends identified by the first layer. From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. The spatial perspective complements the search perspective, forming a coherent framework that systematically integrates global, local, and historical information. Experimental results indicate that the proposed algorithm performs better than previous state-of-the-art methods.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101876"},"PeriodicalIF":8.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534998","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 high-dimensional feature selection algorithm via fast dimensionality reduction and multi-objective differential evolution","authors":"Xuezhi Yue ,&nbsp;Yihang Liao ,&nbsp;Hu Peng ,&nbsp;Lanlan Kang ,&nbsp;Yuan Zeng","doi":"10.1016/j.swevo.2025.101899","DOIUrl":"10.1016/j.swevo.2025.101899","url":null,"abstract":"<div><div>The multi-objective feature selection problem typically involves two key objectives: minimizing the number of selected features and maximizing classification performance. However, most multi-objective evolutionary algorithms (MOEAs) face challenges in high-dimensional datasets, including low search efficiency and potential loss of search space. To address these challenges, this paper proposes a hybrid algorithm based on fast dimensionality reduction and multi-objective differential evolution with redundant and preference processing (termed DR-RPMODE). In DR-RPMODE, the DR phase uses the freezing and activation operators to remove many irrelevant and redundant features in the high-dimensional datasets, thereby achieving fast dimensionality reduction. Subsequently, the RPMODE algorithm continues the search on the reduced datasets, improving the traditional differential evolutionary framework from two aspects: duplicated and redundant solutions are filtered by redundant handling, and a preference handling method that pays more attention to classification performance is designed for different preference objectives of decision-makers. In the experiment, DR-RPMODE is compared with seven feature selection algorithms on 16 classification datasets. The results indicate that DR-RPMODE outperforms the comparison algorithms on most datasets, demonstrating that it not only achieves outstanding optimization performance but also obtains good classification and scalability results.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101899"},"PeriodicalIF":8.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551979","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":"EABC-AS: Elite-driven artificial bee colony algorithm with adaptive population scaling","authors":"Ruiyang Lin ,&nbsp;Zesong Xu ,&nbsp;Liyang Yu ,&nbsp;Tongquan Wei","doi":"10.1016/j.swevo.2025.101893","DOIUrl":"10.1016/j.swevo.2025.101893","url":null,"abstract":"<div><div>The Artificial Bee Colony Algorithm (ABC) is a widely recognized optimization algorithm known for its effectiveness. However, many variants of the ABC algorithm fail to fully leverage the potential of each population, and their inherent random search strategies often limit the algorithm’s convergence capabilities, leading to diminished performance. To address these issues, we introduce an enhanced version of the ABC algorithm, which incorporates two essential features: adaptive population scaling and an elite-driven evolutionary strategy. The adaptive population scaling mechanism dynamically adjusts the population size of each bee colony based on their respective function, and the elite-driven evolutionary strategy with external archive makes bees evolve by utilizing information from elite individuals while ensuring diversity is maintained. These two features enhance the algorithm’s convergence ability. We employ the CEC 2017 and CEC 2022 benchmarks to assess the optimization capabilities of the proposed algorithm. The experimental results indicate that the EABC-AS algorithm displays significant competitiveness relative to CEC excellent algorithms and other state-of-the-art (SOTA) algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101893"},"PeriodicalIF":8.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534999","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":"Benchmarking footprints of continuous black-box optimization algorithms: Explainable insights into algorithm success and failure","authors":"Ana Nikolikj ,&nbsp;Mario Andrés Muñoz ,&nbsp;Tome Eftimov","doi":"10.1016/j.swevo.2025.101895","DOIUrl":"10.1016/j.swevo.2025.101895","url":null,"abstract":"<div><div>The practices for comparing black-box optimization algorithms based on performance statistics over a benchmark suite are being increasingly criticized. Critics argue that these practices fail to explain why particular algorithms outperform others. Consequently, there is a growing demand for more robust comparison methods that assess the overall efficiency of the algorithms in terms of performance and also consider the specific landscape properties of the optimization problems on which the algorithms are compared. This study introduces a novel approach for comparing algorithms based on the concept of an <em>algorithm footprint</em>, which aims to identify easy and challenging problem instances for a given algorithm. A unique footprint is assigned to each algorithm and then compared, to highlight problem instances where an algorithm either uniquely succeeds or falls, as well as how the algorithms complement each other across the problem instances. Our solution employs a multi-task regression model (MTR) to simultaneously link the performance of multiple algorithms with the landscape features of the problem instances. By applying an Explainable Machine Learning (XML) technique, we quantify and compare the importance of the landscape features for each algorithm. The methodology is applied to a portfolio of three different BBO algorithms, highlighting their success and failure on the Black-Box Optimization Benchmarking (BBOB) suite. The efficacy of our approach is further demonstrated through a comparative analysis with two existing algorithm comparison methods, showcasing the robustness and depth of insights provided by the proposed approach.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101895"},"PeriodicalIF":8.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-space adaptive exploration for explainable particle swarm optimization","authors":"Mehdi Alimohammadi,&nbsp;Mohammad-R. Akbarzadeh-T","doi":"10.1016/j.swevo.2025.101868","DOIUrl":"10.1016/j.swevo.2025.101868","url":null,"abstract":"<div><div>A systems theory framework for swarm optimization algorithms promises the rigorous analysis of swarm behaviors and systematic approaches that could avoid ad hoc parameter settings and achieve guaranteed performances. However, optimization processes must treat various systems theory concepts, such as stability and controllability, differently, as swarm optimization relies on preserving diversity rather than reaching uniform agent behavior. This work addresses this duality of perspective and proposes State-Space Particle Swarm Optimization (SS-PSO) using the feedback concept in control systems theory. By exploiting the hidden analogy between these two paradigms, we introduce the concept of controllability for optimization purposes through state-space representation. Extending controllability to particle swarm optimization (PSO) highlights the ability to span the search space, emphasizing the significance of particles' movement rather than their loss of diversity. Furthermore, adaptive exploration (AE) using an iterative bisection algorithm is proposed for the PSO parameters that leverages this controllability measure and its minimum singular value to facilitate explainable swarm behaviors and escape local minima. Theoretical and numerical analyses reveal that SS-PSO is only uncontrollable when the cognitive factor is zero, implying less exploration. Finally, AE enhances exploration by increasing the controllability matrix's minimum singular value. This result underscores the profound connection between the controllability matrix and exploration, a critical insight that significantly enhances our understanding of swarm optimization. AE-based State-Space-PSO (AESS-PSO) shows improved exploration and performance over PSO in 86 SOP and CEC benchmarks, particularly for smaller populations.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101868"},"PeriodicalIF":8.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528710","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-objective genetic programming based on decomposition for feature learning in image classification","authors":"Tuo Zhang ,&nbsp;Ying Bi ,&nbsp;Jing Liang ,&nbsp;Bing Xue ,&nbsp;Mengjie Zhang","doi":"10.1016/j.swevo.2025.101875","DOIUrl":"10.1016/j.swevo.2025.101875","url":null,"abstract":"<div><div>Image classification presents a challenge due to its high dimensionality and extensive variations. Feature learning is a powerful method in addressing this challenge, constituting a multi-objective problem aimed at maximizing classification accuracy and minimizing the number of learned features. A few multi-objective genetic programming (MOGP) methods have been proposed to optimize these two objectives, simultaneously. However, existing MOGP methods ignore the characteristics of feature learning tasks. Therefore, this work proposes a decomposition-based MOGP approach with a global replacement strategy for feature learning in data-efficient image classification. To handle the different value ranges of the two objectives, a transformation function is designed to uniform the range of the number of learned features. In addition, a preference-based decomposition strategy is proposed to address the preference for the objective of classification accuracy. The proposed approach is compared with existing MOGP methods for feature learning on five different image classification datasets with different numbers of training images. The experimental results demonstrate the effectiveness of the proposed approach by achieving better HVs than or comparable to the existing MOGP methods in at least 13 out of 20 cases and classification accuracy significantly better than a popular neural architecture search method in all cases.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101875"},"PeriodicalIF":8.2,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527381","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":"Landscape features in single-objective continuous optimization: Have we hit a wall in algorithm selection generalization?","authors":"Gjorgjina Cenikj ,&nbsp;Gašper Petelin ,&nbsp;Moritz Seiler ,&nbsp;Nikola Cenikj ,&nbsp;Tome Eftimov","doi":"10.1016/j.swevo.2025.101894","DOIUrl":"10.1016/j.swevo.2025.101894","url":null,"abstract":"<div><div>The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a Single Best Solver.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101894"},"PeriodicalIF":8.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart cities optimization using computational intelligence in power-constrained IoT sensor networks","authors":"Khalid A. Darabkh,&nbsp;Muna Al-Akhras","doi":"10.1016/j.swevo.2025.101889","DOIUrl":"10.1016/j.swevo.2025.101889","url":null,"abstract":"<div><div>This paper introduces the Innovative Clustering Energy Efficient Equilibrium Optimizer-based Multi-Hop Routing Protocol (ICEE-EO-MHRP) for addressing the energy constraint in Internet of Things (IoT) network clustering utilizing the Equilibrium Optimizer (EO), a yet efficient computational intelligence method that is used for selecting Designated Cluster Head (DCH) and Backup DCH (BDCH). Additionally, ICEE-EO-MHRP deals with the IoT energy problem by incorporating a novel cost function that ends up of selecting Designated Relays (DRs) and backup DRs for the purpose of forwarding the traffic towards the sink node. Our protocol substantially reduces messages’ exchanges between IoT Sensor Nodes (SNs) by making the replacement of DCH and BDCH dependent on their energy levels dropping below a threshold. To ensure a balanced communication load and efficient scheduling, an innovative deterministic distributed-time division multiple access system is employed. Not only to this extent, but we address data redundancy issue, raised among those quite adjacent SNs, and accordingly propose an efficient management that guarantees having a coherent protocol. In addition to that, device and link failures are professionally addressed by suggesting recovery mechanisms that optimize the proposed protocol. Dealing with these impairments puts our approach well ahead of the competition since it addresses the most practical issues and scenarios, particularly those with challenging environmental constraints. The simulation results demonstrate primarily that our protocol significantly improves the network lifetime by 157.83 % and 109.81 % in comparison to Particle Swarm Optimization and Tabu Search (Tabu-PSO) and Energy-Efficient CH Selection by Improved Sparrow Search Algorithm utilizing Differential Evolution (EECHS-ISSADE), respectively. Comparing ICEE-EO-MHRP to Tabu-PSO and EECHS-ISSADE reveals improvements in residual energy of 335.87 % and 230.05 %, respectively. Furthermore, in comparison to Tabu-PSO and EECHS-ISSADE, the proposed protocol optimizes the throughput by 252.36 % and 168.64 %, respectively. In terms of average delay, our protocol outperforms Tabu-PSO, EECHS-ISSADE, PEGASIS with Artificial Bee Colony (PEG-ABC), Metaheuristics Cluster-based Routing Technique for Energy-Efficient WSN (MHCRT-EEWSN), as well as Hybrid Bald Eagle Search Optimization Algorithm (HBESAOA) by improvements of 57.53 %, 55.15 %, 86.89 %, 20.52 %, and 94.60 %, respectively.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101889"},"PeriodicalIF":8.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512257","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}