Swarm and Evolutionary Computation最新文献

{"title":"Swarm-intelligence-based value iteration for optimal regulation of continuous-time nonlinear systems","authors":"Ding Wang,&nbsp;Qinna Hu,&nbsp;Ao Liu,&nbsp;Junfei Qiao","doi":"10.1016/j.swevo.2025.101913","DOIUrl":"10.1016/j.swevo.2025.101913","url":null,"abstract":"<div><div>In this article, a swarm-intelligence-based value iteration (VI) algorithm is constructed to resolve the optimal control issue for continuous-time (CT) nonlinear systems. By leveraging the evolutionary concept of particle swarm optimization (PSO), the challenge of gradient vanishing is effectively overcome compared to traditional adaptive dynamic programming (ADP). Specifically, a PSO-based action network is implemented to perform policy improvement, eliminating the reliance on gradient information. Furthermore, within the ADP framework, the swarm-intelligence-based VI algorithm for CT systems is developed to address the challenges associated with constraints of initial admissible conditions and the difficulty of selecting probing signals in the traditional policy iteration method. The theoretical analysis is provided to show the convergence of the developed VI algorithm and the stability of the closed-loop system, respectively. Finally, under affine and non-affine backgrounds, two simulations are conducted to demonstrate the effectiveness and optimality of the established swarm-intelligence-based VI scheme for CT systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101913"},"PeriodicalIF":8.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687666","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":"Optimizing MapReduce efficiency and reducing complexity with enhanced particle Swarm Optimization (MR-MPSO)","authors":"Chander Diwaker ,&nbsp;Vijay Hasanpuri ,&nbsp;Yonis Gulzar ,&nbsp;Bhanu Sharma","doi":"10.1016/j.swevo.2025.101917","DOIUrl":"10.1016/j.swevo.2025.101917","url":null,"abstract":"<div><div>Big data's explosive growth poses serious data management difficulties, especially given the data's unequal distribution across massive databases. Because of this mismatch, traditional software systems are less effective, which leads to complex and wasteful data processing. We provide MapReduce-Modified Particle Swarm Optimization (MR-MPSO), a unique optimization technique, to tackle this problem. This strategy not only improves the administration of enormous datasets but also tackles the complexity issue of data imbalance. The MR framework is used to handle large-scale data processing tasks, with MR-MPSO driving the map and reducing functions. Our technique combines adaptive inertia weight with Particle Swarm Optimization (PSO) to improve the accuracy and efficiency of optimization for 10 unimodal and multimodal benchmark functions. MR-MPSO outperforms four optimization algorithms—MR K-means, MR bat, MR whale, and regular MR-on measures such as fitness value mean, median, and standard deviation. Furthermore, MR-MPSO regularly enhances throughput and average I/O rate, especially in complex write operations, with gains ranging from 1.4 % to 28.9 % in throughput and 2.1 % to 17.7 % in I/O rate over typical MR approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101917"},"PeriodicalIF":8.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687668","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 cooperative discrete artificial bee colony algorithm with Q-learning for solving the distributed permutation flowshop group scheduling problem with preventive maintenance","authors":"Wan-Zhong Wu ,&nbsp;Hong-Yan Sang ,&nbsp;Quan Ke Pan ,&nbsp;Qiu-Yang Han ,&nbsp;Heng-Wei Guo","doi":"10.1016/j.swevo.2025.101910","DOIUrl":"10.1016/j.swevo.2025.101910","url":null,"abstract":"<div><div>With the rapid development of manufacturing technology, the multi-factory production considering group-based job processing and machine maintenance is being given increased focus, due to its potential for enhancing cost efficiency and productivity. Group constraints and machine maintenance play a critical role in modern manufacturing by reducing machine downtime, balancing production loads, and extending equipment lifespan. This paper studies the distributed permutation flowshop group scheduling problem with preventive maintenance (DPFGSP/PM) by proposing a cooperative discrete artificial bee colony (CDABC) algorithm, which is based on cooperative strategy, with the objective of minimizing the total flow time (TFT). A novel heuristic based on the group scheduling principles and TFT optimization is introduced in the initialization phase. In the evolutionary phase, the decomposition strategy and the Q-learning strategy are applied to evolve the populations of jobs and groups. Subsequently, these populations are merged to construct the complete solution, and the evaluation criterion is used to determine whether to expand the solution space. Extensive computational experiments and comparisons with state-of-the-art algorithms demonstrate that the proposed CDABC algorithm is an effective solution for DPFGSP/PM.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101910"},"PeriodicalIF":8.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644309","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 efficient history-guided surrogate models-assisted niching evolutionary algorithm for expensive multimodal optimization","authors":"Ting Huang ,&nbsp;Bing-Bing Niu ,&nbsp;Yue-Jiao Gong ,&nbsp;Jing Liu","doi":"10.1016/j.swevo.2025.101906","DOIUrl":"10.1016/j.swevo.2025.101906","url":null,"abstract":"<div><div>This work addresses the challenge of multimodal optimization, aiming to identify multiple optimal solutions in costly and time-consuming evaluation scenarios, known as expensive multimodal optimization problems (EMMOPs). Existing methods that adopt surrogate models to approximate costly evaluations with challenges, such as high costs of constructing training sets, inaccurate optima detection, and difficulties balancing exploration and exploitation in multimodal landscapes. To address these issues, we propose an efficient Binary Space Partitioning (BSP)-based surrogate models (SMs)-assisted niching evolutionary algorithm (NEA), termed BSP-SMs-NEA. The BSP tree provides a structured method for storing and retrieving historical information, enabling efficient construction of training sets for SMs. The SMs are then adaptively constructed and updated across niches to maintain high accuracy. Furthermore, BSP-SMs assist the NEA in selective evolution, optimizing resource utilization while balancing exploration and exploitation. Compared with 11 existing methods on EMMOP benchmark, BSP-SMs-NEA demonstrates superior performance, achieving the best precision on 80% of test functions, along with the top success rate and statistical results of the best fitness value across all test functions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101906"},"PeriodicalIF":8.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642537","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":"Parrot optimization algorithm for improved multi-strategy fusion for feature optimization of data in medical and industrial field","authors":"Gaoxia Huang ,&nbsp;Jianan Wei ,&nbsp;Yage Yuan ,&nbsp;Haisong Huang ,&nbsp;Hualin Chen","doi":"10.1016/j.swevo.2025.101908","DOIUrl":"10.1016/j.swevo.2025.101908","url":null,"abstract":"<div><div>Feature selection is crucial in machine learning, data mining and pattern recognition, aiming at refining data features and improving model performance. Data features in the medical-industrial field are numerous and often contain redundant and irrelevant information, which affects model efficiency and generalization ability. Given that the superior performance of meta-heuristic algorithms in dealing with complex constrained problems has been demonstrated and many researchers have used them for feature selection to process data with better results than traditional methods, this study innovatively proposes an improved Multi-Strategy Fused Parrot Optimization Algorithm (MIPO) to optimize the feature selection process targeting the medical-industrial data. MIPO incorporates four core mechanisms: first, balanced and optimized foraging behavior to pinpoint key features; second, lens imaging reverse dwell behavior to strengthen local search; third, vertical and horizontal cross-communication behavior to promote population co-evolution; and fourth, memory behavior to intelligently guide the search direction. In addition, the pacifying behavior strategy is introduced to enhance the stability and robustness of the algorithm in complex space. To fully validate MIPO, this paper designs exhaustive experiments, including ablation experiments, experiments comparing with mainstream algorithms and comparisons with other feature selection methods, to demonstrate its superior performance in multiple dimensions. Based on the S/V transfer function, nine binary variants are constructed to cope with the challenge of diverse feature selection. The experimental results show that MIPO and its variants exhibit efficient, general and strong generalization capabilities on 23 medical-industrial datasets. Further, by combining KNN, SVM, and RF classifiers, MIPO significantly improves the model accuracy, with average improvement rates of 55.38%, 35.53%, and 49.59%, respectively, compared with the original parrot algorithm, and the optimal variant also performs well on all types of classifiers, with average improvement rates of 53.91%, 34.38%, and 49.94% for the optimal variant, proving the wide applicability of MIPO. In this study, the adaptability of MIPO and classifiers is deeply explored to provide scientific guidance and practical suggestions for practical applications, which not only promotes the technological progress in the field of feature selection, but also provides a powerful tool for data processing and analysis in the field of medical and industrial.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101908"},"PeriodicalIF":8.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644308","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":"Hierarchical multi-objective optimization for precise performance design of closed-chain legged mechanisms","authors":"Long Guo ,&nbsp;Ying Zhang ,&nbsp;Qi Qin ,&nbsp;Guanjun Liu ,&nbsp;Hanyu Chen ,&nbsp;Yan-an Yao","doi":"10.1016/j.swevo.2025.101904","DOIUrl":"10.1016/j.swevo.2025.101904","url":null,"abstract":"<div><div>Over the past decades, the performance design of closed-chain legged mechanisms (CLMs) has not been adequately addressed. Most existing design methodologies have predominantly relied on trajectory synthesis, which inadvertently prioritizes less critical performance aspects. This study proposes a hierarchical multi-objective optimization strategy to address this limitation. First, the numerical performance-trajectory mapping is derived based on a foot-ground contact model, aiming to decouple the performance characteristics. Subsequently, a hierarchical optimization strategy is employed for two types of CLM design scenarios: In trajectory shape-constrained scenarios, a coarse-to-fine optimization process, integrating Fourier descriptors, refines the design from overall shape to local features. In scenarios without trajectory shape constraints, a stepwise optimization process is proposed for reconfigurable CLMs to transition from primary motion to auxiliary motion. The robustness of the proposed design strategy is validated across three configurations and seven algorithms. The effectiveness of the proposed design strategy is verified by comparison with other existing CLM design methods. The applicability of the proposed strategy is confirmed through simulation and prototype experiments. The results demonstrate that the hierarchical strategy effectively addresses the challenges of precise performance design in CLMs. Our work provides a general framework for the CLM design and offers insights for the optimization design of other closed-chain linkages.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101904"},"PeriodicalIF":8.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637541","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":"Un-evaluated solutions may be valuable in expensive optimization","authors":"Hao Hao ,&nbsp;Xiaoqun Zhang ,&nbsp;Aimin Zhou","doi":"10.1016/j.swevo.2025.101905","DOIUrl":"10.1016/j.swevo.2025.101905","url":null,"abstract":"<div><div>Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study of surrogate-assisted evolutionary algorithms (SAEAs) in EOPs, we discovered an intriguing phenomenon. Because only a limited number of solutions are evaluated in each iteration, relying solely on these evaluated solutions for evolution can lead to reduced disparity in successive populations. This, in turn, hampers the reproduction operators’ ability to generate superior solutions, thereby reducing the algorithm’s convergence speed. To address this issue, we propose a strategic approach that incorporates high-quality, un-evaluated solutions predicted by surrogate models during the selection phase. This approach aims to improve the distribution of evaluated solutions, thereby generating a superior next generation of solutions. This work details specific implementations of this concept across various reproduction operators and validates its effectiveness using multiple surrogate models. Experimental results demonstrate that the proposed strategy significantly enhances the performance of surrogate-assisted evolutionary algorithms. Compared to mainstream SAEAs and Bayesian optimization algorithms, our approach incorporating the un-evaluated solution strategy shows a marked improvement.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101905"},"PeriodicalIF":8.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637577","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 novel deep self-learning method for flexible job-shop scheduling problems with multiplicity: Deep reinforcement learning assisted the fluid master-apprentice evolutionary algorithm","authors":"Linshan Ding ,&nbsp;Dan Luo ,&nbsp;Rauf Mudassar ,&nbsp;Lei Yue ,&nbsp;Leilei Meng","doi":"10.1016/j.swevo.2025.101907","DOIUrl":"10.1016/j.swevo.2025.101907","url":null,"abstract":"<div><div>In today’s dynamic environment, companies must navigate highly competitive markets. They consistently need to implement new technologies and deliver the right product at the right time in response to customer demand. This necessitates a high level of adaptability and efficiency in their manufacturing processes. Flexible job-shops offer a more efficient alternative to traditional manufacturing practices by accommodating these needs. Additionally, in actual manufacturing plants, multiple jobs are typically required for each part type. To address this complexity, this article investigates the flexible job-shop scheduling problem with multiplicity (MFJSP). We propose a deep self-learning method based on deep reinforcement learning and fluid master-apprentice evolutionary algorithm (DSLFMAE) to minimize makespan for the MFJSP. The proposed DSLFMAE is the integration of a fluid master-apprentice evolutionary (FMAE) algorithm and a proximal policy optimization (PPO) algorithm. The FMAE algorithm serves as the core optimization method, employing the PPO algorithm to dynamically adjust the control parameters of the FMAE algorithm during the optimization process. Twelve state features are extracted to capture the evolutionary states of the FMAE algorithm accurately, and a long short-term memory Q-network (LSTM-Q) is designed to encode these continuous states. Subsequently, to adjust multiple interrelated control parameters of the FMAE algorithm simultaneously, a multivariate Gaussian distribution-based PPO algorithm is developed to train the LSTM-Q network. Numerical outcomes show the efficacy and superiority of the DSLFMAE in addressing the flexible job-shop scheduling problem with multiplicity (MFJSP) across different scales.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101907"},"PeriodicalIF":8.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620161","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":"Constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints","authors":"Jianxia Li,&nbsp;Ruochen Liu,&nbsp;Xilong Zhang,&nbsp;Ruinan Wang","doi":"10.1016/j.swevo.2025.101903","DOIUrl":"10.1016/j.swevo.2025.101903","url":null,"abstract":"<div><div>Many engineering optimization problems require simultaneous optimization of multiple objective functions under certain constraints, which are collectively referred to as constrained multi-objective problems (CMOPs). The crucial issue in solving CMOPs is to balance constraints and objectives. This paper proposes a constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints, termed CORCMO. CORCMO mainly comprises two stages: the learning stage and the evolving stage. The learning stage focuses on analyzing the correlation between each objective and constraints. In the evolving stage, the CMOP is decomposed into <em>M</em> constraint single-objective problems, which are optimized by <em>M</em> subpopulations cooperatively. For each subproblem, the corresponding fitness function, computed based on the correlation, is adopted to guide the evolution. Subsequently, CORCMO employs archive population update strategy to find the optimal solutions of the given CMOP. Experiments conducted on a series of benchmark problems demonstrate that CORCMO is promising to solve CMOPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101903"},"PeriodicalIF":8.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620322","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":"Gaussian process regression for evolutionary dynamic multiobjective optimization in complex environments","authors":"Youpeng Deng ,&nbsp;Yan Zheng ,&nbsp;Zhaopeng Meng ,&nbsp;Haobo Gao ,&nbsp;Yueyang Hua ,&nbsp;Qiangguo Jin ,&nbsp;Leilei Cao","doi":"10.1016/j.swevo.2025.101883","DOIUrl":"10.1016/j.swevo.2025.101883","url":null,"abstract":"<div><div>Multiobjective Evolutionary Algorithms (MOEAs) face significant challenges when addressing dynamic multiobjective optimization problems, particularly those with frequent changes. The complexity of dynamic environments makes it difficult for MOEAs to accurately approximate the true Pareto-optimal solutions before subsequent changes occur. Typically, historical approximations of Pareto-optimal solutions are utilized to predict solutions in future environments. However, existing predictors often overlook the nondeterministic nature of historical solutions, potentially compromising prediction accuracy. In this paper, we propose a novel predictor based on Gaussian Process Regression (GPR) for evolutionary dynamic multiobjective optimization. Unlike traditional deterministic predictors, our approach aims to provide a probability distribution of predicted results, thereby addressing the inherent nondeterminism of historical solutions. We employ GPR to model relationships among historical solutions across different time steps. Within the framework of the classical MOEA, MOEA/D, we introduce a new method MOEA/D-GPR for Evolutionary Dynamic Multiobjective Optimization (EDMO). Experimental results demonstrate that our method achieves state-of-the-art performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101883"},"PeriodicalIF":8.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620239","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}