Swarm and Evolutionary Computation最新文献

{"title":"Dynamic quadratic decomposition-based evolutionary algorithm for multi-objective fuzzy flexible jobshop scheduling","authors":"XuWei Zhang ,&nbsp;ZiYan Zhao ,&nbsp;ShuJin Qin ,&nbsp;ShiXin Liu ,&nbsp;MengChu Zhou","doi":"10.1016/j.swevo.2025.101884","DOIUrl":"10.1016/j.swevo.2025.101884","url":null,"abstract":"<div><div>Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs) have garnered widespread attention since they are able to handle the uncertainty of processing time in actual production. Nevertheless, making a good balance between the diversity and convergence of non-dominated solutions is a challenging issue that cannot be overlooked when MFFJSP is solved. To deal with these issues, this work proposes a Dynamic Quadratic Decomposition-based Multi-objective Evolutionary Algorithm (DQD-MOEA) to solve MFFJSP by minimizing makespan and total machine workload. To solve a problem that the distribution and diversity of searched non-dominant solutions are poor due to the discrete decision space and objective space of MFFJSP, it proposes a dynamic quadratic decomposition method. Its core idea is to eliminate all the failed reference vectors because they have no intersection with a real Pareto front, and ensure that solutions evolve along effective reference vectors. This work also introduces a problem-specific local search method to accelerate the solution convergence for MFFJSP. It proposes a hybrid initialization method to improve the quality of initial solutions. Finally, a series of experiments are performed and the results demonstrate that DQD-MOEA is significantly better than state-of-the-art algorithms in terms of convergence and solution diversity when solving widely-tested benchmark cases.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101884"},"PeriodicalIF":8.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445463","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 particle swarm optimization for bistatic RFID network planning with distributed antennas","authors":"Yamin Wang,&nbsp;Shuai Ma,&nbsp;Yuan Li,&nbsp;Hongyu Qian,&nbsp;Qianfan Jia,&nbsp;Shanpeng Xiao,&nbsp;Yuhong Huang","doi":"10.1016/j.swevo.2025.101882","DOIUrl":"10.1016/j.swevo.2025.101882","url":null,"abstract":"<div><div>Radio Frequency Identification (RFID) network planning (RNP) is crucial for optimizing network performance by setting system parameters. The new bistatic RFID architecture with a distributed antenna system (DAS) offers advantages for the passive Internet of Things (IoT). It separates transmission and reception to minimize self-interference and extend uplink communication range, while using distributed antennas for broader coverage. Bistatic DAS RNP differs from monostatic in various aspects. Monostatic RNP focuses on factors like reader number, location, and power, while bistatic DAS RNP involves more parameters, including antenna and device numbers, locations, and interconnections. Coverage and interference are more complex, and practical planning faces constraints on antenna ports and feeder line length. Consequently, bistatic DAS RFID network planning (BDRNP) problems are novel, complex, high-dimensional, and constrained, making them relatively unexplored and highly challenging. This paper analyzes bistatic DAS RFID network coverage and interference, and proposes a mathematical model for BDRNP problems. A modified multi-objective discrete particle optimization (M2DPSO) algorithm is introduced, incorporating a modified k-means clustering method to group antennas, which ensures satisfaction constraints and reduces decision variable dimensionality from <span><math><mrow><mn>4</mn><mrow><mo>|</mo><mi>C</mi><mi>S</mi><mo>|</mo></mrow><mo>+</mo><msup><mrow><mrow><mo>|</mo><mi>C</mi><mi>S</mi><mo>|</mo></mrow></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> to <span><math><mrow><mn>4</mn><mrow><mo>|</mo><mi>C</mi><mi>S</mi><mo>|</mo></mrow></mrow></math></span> to <span><math><mrow><mn>4</mn><mrow><mo>|</mo><mi>C</mi><mi>S</mi><mo>|</mo></mrow></mrow></math></span> where <span><math><mrow><mo>|</mo><mi>C</mi><mi>S</mi><mo>|</mo></mrow></math></span> is the problem size. Redundant SDRs/carrier emitters are dynamically eliminated based on global best solution set changes. Experimental results show that M2DPSO algorithm significantly outperforms three existing popular algorithms – nondominated sorting genetic algorithm II (NSGAII), discrete particle swarm optimization (DPSO), and multi-objective evolutionary algorithm based on decomposition (MOEAD) – by 265%, 361%, and 726% respectively, in average inverted generational distance (IGD) metrics.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101882"},"PeriodicalIF":8.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437477","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":"Strengthened grey wolf optimization algorithms for numerical optimization tasks and AutoML","authors":"Xuefen Chen,&nbsp;Chunming Ye,&nbsp;Yang Zhang","doi":"10.1016/j.swevo.2025.101891","DOIUrl":"10.1016/j.swevo.2025.101891","url":null,"abstract":"<div><div>The grey wolf optimization algorithm (GWO) is an efficient optimization technology. However, it still has some problems such as immature convergence and stagnation at local optima. In this paper, a strengthened grey wolf optimization algorithm (SGWO) is proposed based on three strengthening mechanisms: the exponential decreasing convergence factor, the elite reselection strategy in per generation and the Cauchy mutation (CM) operator. Seven variants of SGWO are designed according to different deployment modes of three reinforcement mechanisms. Experiments on thirteen numerical optimization problems are carried out to compare the differences between GWO and SGWOs. The experimental results reveal that SGWOs can significantly improve the search performance of GWO in most tasks. Among them, SGWO7 is the most successful competitor. Furthermore, several optimizers have demonstrated through comparison on engineering design problems that SGWO7 outperforms the vast majority of competitors. Subsequently, MHHO, TLBO, GWO and SGWO7 are used to build automatic machine learning (AutoML) model. The experimental results of the four methods on MNIST dataset further illustrate the advantages of SGWO7 designed in this research.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101891"},"PeriodicalIF":8.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437476","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":"Novel MINLP model and Lamarckian learning-enhanced multi-objective optimization algorithm for smart household appliance scheduling","authors":"Weidong Lei ,&nbsp;Ziheng You ,&nbsp;Jiawei Zhu ,&nbsp;Pengyu Yan ,&nbsp;Zhen Zhou ,&nbsp;Jikun Chen","doi":"10.1016/j.swevo.2025.101886","DOIUrl":"10.1016/j.swevo.2025.101886","url":null,"abstract":"<div><div>With the rapid development of information and communication technology, a home energy management system (HEMS) on the demand side, embedded with advanced scheduling models and optimization algorithms, has the potential to conserve energy, reduce users’ electricity costs and dissatisfaction, while ensuring the stable operation of the power grid. This paper first develops a novel mixed-integer non-linear programming (MINLP) model for the smart household appliance scheduling problem with solar energy and energy storage to minimize the total electricity consumption cost and the user dissatisfaction simultaneously over a day. Next, to the best of our knowledge, this is the first work to propose a novel Lamarckian-learning enhanced multi-objective particle swarm optimization (LLMOPSO) algorithm to solve the studied problem. To validate the effectiveness of the improved model and algorithm, comparative experiments are conducted on four case studies under different scenarios. The experimental results demonstrate that the proposed LLMOPSO outperforms the existing ones in terms of eight commonly used performance metrics, such as the number of non-dominated solutions (<em>ND</em>), the ratio of non-dominated solutions (<em>R_nd</em>), the generational distance (<em>GD</em>), and the metric of diversity (<em>DM</em>). Compared to four existing optimization algorithms, our novel approach can provide better schedules for users, enabling them to manage smart household appliances in a more flexible, comfortable, and cost-effective way.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101886"},"PeriodicalIF":8.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428904","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":"Solving sparse multi-objective optimization problems via dynamic adaptive grouping and reward-penalty sparse strategies","authors":"Zhenxing Yu ,&nbsp;Qinwei Fan ,&nbsp;Jacek M. Zurada ,&nbsp;Jigen Peng ,&nbsp;Haiyang Li ,&nbsp;Jian Wang","doi":"10.1016/j.swevo.2025.101881","DOIUrl":"10.1016/j.swevo.2025.101881","url":null,"abstract":"<div><div>Sparse Multi-Objective Optimization Problems (SMOPs) are commonly encountered in various fields such as machine learning, signal processing, and data mining. While evolutionary algorithms have shown good performance in tackling complex problems, many algorithms tend to exhibit performance degradation when dealing with SMOPs. The primary reasons for this performance decline are the curse of dimensionality and the inability to effectively leverage the sparsity of Pareto-optimal solutions. To address this issue, this paper proposes a model method to solve sparse multi-objective optimization problems through dynamic adaptive grouping and reward-penalty sparse strategies. Specifically, to obtain more effective prior information, a sparse initialization strategy is proposed in the initialization phase. This strategy aims to incorporate more prior knowledge and information about the sparsity of Pareto-optimal solutions. In the evolutionary phase, a decision variable dynamic adaptive grouping strategy is introduced. This strategy, combined with crossover and mutation operators, guides the population towards effective sparse directions. Furthermore, to further identify zero-value decision variables in Pareto-optimal solutions, a reward-penalty mechanism is designed to update the scores of decision variables. By combining this mechanism with the adaptive grouping strategy, this method can effectively flip low-scoring decision variables to zero with a higher probability. To validate the advantages of the proposed algorithm, experiments were conducted on eight benchmark problems, with comparative experiments conducted for different initialization methods. The results indicate that our algorithm exhibits significant advantages in solving SMOPs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101881"},"PeriodicalIF":8.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437475","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":"Data Stream driven evolutionary algorithm for cost sensitive robust optimization over time","authors":"Zhening Liu ,&nbsp;Handing Wang ,&nbsp;Jinliang Ding ,&nbsp;Cuie Yang ,&nbsp;Yaochu Jin","doi":"10.1016/j.swevo.2025.101880","DOIUrl":"10.1016/j.swevo.2025.101880","url":null,"abstract":"<div><div>Many dynamic optimization problems in real-world domains like engineering and management science require considerations of robustness, where a balance between tracking optimal solutions in changing environments and managing costs of switching solutions is needed. However, in some cases, the objective functions are not analytically available and must be approximated based on data collected from numerical simulations or experiments. These dynamic problems are formulated as data stream driven robust optimization over time (DDROOT<span><math><msub><mrow></mrow><mrow><mi>G</mi></mrow></msub></math></span>) problems, which cannot be satisfactorily addressed by existing dynamic optimization algorithms. Therefore, we propose a data stream driven multi-form evolutionary algorithm (DDMFEA), employing two separate Kriging models to approximate the unavailable objective function and the computationally expensive robustness estimation, respectively. In the proposed algorithm, DDROOT<span><math><msub><mrow></mrow><mrow><mi>G</mi></mrow></msub></math></span> problems are addressed with two distinct formulations with single- and multi-objectives. These formulations are utilized as a multi-form optimization process to mitigate the impact of approximation errors from both Kriging models. In addition, a novel solution selection mechanism is designed to consider both robustness and predicted objective values, facilitating the deployment of the optimal robust solution. Throughout the experiment, four robust comparison algorithms are employed to assess the performance of the proposed DDMFEA across various problems in different decision dimensions. The experimental results validate the significance of each proposed contribution and demonstrate the exceptional performance of DDMFEA.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101880"},"PeriodicalIF":8.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421530","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":"Surrogate-assisted differential evolution: A survey","authors":"Laiqi Yu ,&nbsp;Zhenyu Meng ,&nbsp;Lingping Kong ,&nbsp;Vaclav Snasel ,&nbsp;Jeng-Shyang Pan","doi":"10.1016/j.swevo.2025.101879","DOIUrl":"10.1016/j.swevo.2025.101879","url":null,"abstract":"<div><div>Expensive Optimization Problems (EOPs) are a pressing challenge in real-world applications because they require high-quality solutions under tight computational budgets. To tackle this, numerous Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proposed that combine Evolutionary Algorithms (EAs) with surrogate models. Recently, researchers have conducted systematic surveys on SAEAs to better showcase their potential in solving EOPs. However, most of these efforts have focused on surrogate models, while largely overlooking EAs. This imbalance poses a challenge to the long-term development of SAEAs. Among various SAEAs, Surrogate-Assisted Differential Evolution (SADE) is widely favored by researchers due to the competitive performance of DE in Evolutionary Computation. It has been broadly applied across diverse engineering and scientific domains. Nevertheless, there is still no work that systematically investigates the progress of SADE. To balance the research direction of SAEAs and fill the gap, this paper provides a comprehensive survey of SADE. Its contributions are summarized as follows: This paper first introduces the general optimization framework of SAEAs and briefly reviews the research directions and advances of its key components. Next, a comprehensive survey of SADE is conducted, covering commonly used surrogate models and DE algorithms. It also examines how existing SADE algorithms use DE, performance evaluation methods, and real-world applications. Finally, future challenges and potential research directions are discussed. We hope this work will draw attention to EAs and inspire further research to advance related fields.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101879"},"PeriodicalIF":8.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421606","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":"Evolutionary algorithm based on multi-probability distribution model for stochastic optimization","authors":"Hao Cong ,&nbsp;Xiao-Min Hu ,&nbsp;Wei-Neng Chen ,&nbsp;Wen Shi ,&nbsp;Jun Zhang","doi":"10.1016/j.swevo.2024.101839","DOIUrl":"10.1016/j.swevo.2024.101839","url":null,"abstract":"<div><div>Stochastic optimization, which aims at optimizing the expected value of a stochastic objective function, is challenging and commonly-seen in engineering applications. One crucial challenge of stochastic optimization problems (SOPs) is that the objective function value is impossible to calculate accurately due to the existence of uncertainty. As probability distribution is a common mathematical tool for handling uncertainty, this paper intends to explore the use of probability-distribution-based evolutionary algorithms (EAs) for solving complicated SOPs. First, an in-depth analysis of how to sample and construct probability distributions for probability-distribution-based EAs in SOPs is performed through both empirical and theoretical studies. Based on the analysis, it can be concluded that the implicit averaging method is helpful for probability-distribution-based EAs to solve SOPs. Second, evolutionary algorithm based on multiple probability distribution models (EA-mPD) framework is proposed. Instead of using a single probability distribution, the whole population is divided into several clusters by clustering, and several local probability models are built for different clusters. Finally, probability-distribution-based EAs such as estimation of distribution algorithm (EDA) and ant colony optimization (ACO) are introduced in the proposed EA-mPD to solve SOPs. Experimental results show that the proposed EA-mPD method is promising in terms of both accuracy and efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101839"},"PeriodicalIF":8.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551983","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":"Neuro-PSO algorithm for large-scale dynamic optimization","authors":"Mohamed Radwan ,&nbsp;Saber Elsayed ,&nbsp;Ruhul Sarker ,&nbsp;Daryl Essam ,&nbsp;Carlos Coello Coello","doi":"10.1016/j.swevo.2025.101865","DOIUrl":"10.1016/j.swevo.2025.101865","url":null,"abstract":"<div><div>Over the last few decades, dynamic optimization and large-scale optimization have been two challenging research topics. In this context, dynamic optimization with high dimensionality is undoubtedly another important research topic. For such a combined problem, this paper develops: (1) an algorithm that incorporates problem decomposition to deal with high dimensionality, (2) a search algorithm for optimization, and (3) a prediction strategy to deal with dynamic changes. Firstly, a decomposition method is introduced to divide the problem into multiple subproblems based on the level of interactions among the decision variables. For optimization, a multi-population search algorithm is proposed, where each subpopulation evolves individually. Finally, a machine learning-based prediction strategy is developed to learn information from historical solutions and predict some solutions that may be useful for the new environment. The proposed algorithm is tested using the generalized moving peaks benchmark problems. The results show that the proposed algorithm can find better solutions than existing approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101865"},"PeriodicalIF":8.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387424","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":"DANCE: Distributed co-evolutionary design of velocity controllers for swarm intelligence robots in flocking and entrapping tasks","authors":"Chen Wang ,&nbsp;Cheng Zhu ,&nbsp;Xianqiang Zhu ,&nbsp;Hongtao Lei ,&nbsp;Weiming Zhang ,&nbsp;Meng Wu","doi":"10.1016/j.swevo.2025.101854","DOIUrl":"10.1016/j.swevo.2025.101854","url":null,"abstract":"<div><div>This study combined evolutionary algorithm and reinforcement learning to propose a new automated design method for generating swarm robots velocity controller model. It alternately evolves heterogeneous swarm and homogeneous swarm through a gene expression programming method that introduces reinforcement learning, and assembles function nodes and leaf nodes into new mathematical formulas during the evolution process. The method enable to realize the effect of swarm robots emerging to perform swarm tasks such as flocking and entrapping. What is more, a new swarm rule was discovered during the evolution process, which is used to realize the flocking of swarm robots at any angle. The experimental results show that the swarm motion controller automatically generated by the model has high task completion efficiency and strong generalization.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101854"},"PeriodicalIF":8.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377676","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}