Swarm and Evolutionary Computation最新文献_第4页

Parallel fractional dominance MOEAs for feature subset selection in big data 用于大数据特征子集选择的并行分数优势 MOEAs

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1016/j.swevo.2024.101687

{"title":"Parallel fractional dominance MOEAs for feature subset selection in big data","authors":"","doi":"10.1016/j.swevo.2024.101687","DOIUrl":"10.1016/j.swevo.2024.101687","url":null,"abstract":"<div><p>In this paper, we solve the feature subset selection (FSS) problem with three objective functions namely, cardinality, area under receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC) using novel multi-objective evolutionary algorithms (MOEAs). MOEAs often encounter poor convergence due to the increase in non-dominated solutions and getting entrapped in the local optima. This situation worsens when dealing with large, voluminous big and high-dimensional datasets. To address these challenges, we propose parallel, fractional dominance-based MOEAs for FSS under Spark. Further, to improve the exploitation of MOEAs, we introduce a novel batch opposition-based learning (BOP) along with a cardinality constraint on the opposite solution. Accordingly, we propose two variants, namely, BOP1 and BOP2. In BOP1, a single neighbour is randomly chosen in the opposite solution space, whereas in BOP2, a group of randomly chosen neighbours in the opposite solution space. In either case, the opposite solutions are evaluated to improve the exploitation capability of the underlying MOEAs. We observe that in terms of mean optimal objective function values and across all datasets, the proposed BOP2 variant of parallel fractional dominance-based algorithms emerges as the top performer in obtaining efficient solutions. Further, we introduce a novel metric, namely the ratio of hypervolume (HV) and inverted generated distance (IGD), HV/IGD, that combines both diversity and convergence. With respect to the mean HV/IGD computed over 20 runs and Formula 1 racing, the BOP1 variants of fractional dominance-based MOEAs outperformed other algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm 基于动态涡流搜索算法解决大型自动化仓库的存储位置分配问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1016/j.swevo.2024.101725

{"title":"Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm","authors":"","doi":"10.1016/j.swevo.2024.101725","DOIUrl":"10.1016/j.swevo.2024.101725","url":null,"abstract":"<div><p>This paper establishes the mathematical model for Storage Location Assignment (SLA) problem in large-scale automated warehouses by combining three objectives: efficiency, shelf stability, and stacker load balancing. Along with a novel repair strategy to handle the complex constraints of large-scale problems. Additionally, a coding method and solution approach suitable for practical application scenarios are developed. In order to solve large-scale SLA problem, an improved vortex search algorithm is proposed based on attraction operation in flow field, dimension-by-dimension dynamic radius and leadership decision-making mechanism (FDVSA). In the experimental part, the algorithm effectiveness experiment of FDVSA was first conducted using the large-scale global optimization test sets IEEE congress on evolutionary computation 2010 and 2013 (CEC2010, CEC2013). The results show that: (1) Compared with other comparison algorithms, the comprehensive average optimization rate of FDVSA in CEC2010 and CEC2013 is 88 % and 78 %, respectively. (2) The experimental results of FDVSA showed that each improvement strategy has advantages in dealing with large-scale problems. (3) The post-hoc analysis showed that there are significant differences between FDVSA and other comparison algorithms, and FDVSA is significantly better. Finally, FDVSA and other comparison algorithms are solved on three different scale and complexity of SLA cases. The results show that: (1) FDVSA has significant advantages in solving large-scale SLA problem, and the comprehensive average optimization rate is 19 %. (2) The convergence curve and boxplot showed that FDVSA has good convergence speed and solving stability. (3) The effectiveness of the repair strategy was verified by experiments in the large-scale SLA problems.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized watermarking scheme based on genetic algorithm and elliptic curve 基于遗传算法和椭圆曲线的优化水印方案

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-09-03 DOI: 10.1016/j.swevo.2024.101723

{"title":"An optimized watermarking scheme based on genetic algorithm and elliptic curve","authors":"","doi":"10.1016/j.swevo.2024.101723","DOIUrl":"10.1016/j.swevo.2024.101723","url":null,"abstract":"<div><p>Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving decomposition-based MOEAs for combinatorial optimisation by intensifying corner weights 通过强化角权重改进基于分解的组合优化 MOEAs

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-30 DOI: 10.1016/j.swevo.2024.101722

{"title":"Improving decomposition-based MOEAs for combinatorial optimisation by intensifying corner weights","authors":"","doi":"10.1016/j.swevo.2024.101722","DOIUrl":"10.1016/j.swevo.2024.101722","url":null,"abstract":"<div><p>In the real world, a class of common problems such as supply chain management, project scheduling, portfolio optimisation and facility location design are multi-objective combinatorial optimisation problems (MOCOPs), where there are multiple objectives and the set of feasible solutions is discrete. In MOCOPs, corner solutions are solutions in which at least one objective reaches the optimal value. Corner solutions are important as they are likely to be preferred by the decision maker and are able to help improve algorithm performance. In this paper, we first reveal that in decomposition-based MOEAs, improving the corner weights (as opposed to improving the middle weights) significantly enhances the generation of corner solutions, thereby enhancing the overall performance of algorithms. Based on this, we propose a method to enhance the search for corner solutions in MOCOPs. We act on a class of popular MOEAs, decomposition-based MOEAs, and in their evolutionary mechanism we intensify the weights in the corner areas. To verify the proposed method, we conduct experiments by incorporating the method into three decomposition-based MOEAs, MOEA/D, MOEA/D-DRA-UT and MOEA/D-LdEA (the latter two were designed specifically for enhancing the search of corner solutions). The experimental results demonstrate that the proposed method can improve the spread of solution sets found, without compromising the quality of internal solutions.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2210650224002608/pdfft?md5=bb078c5eba46bd595de7307ec96bcb7d&pid=1-s2.0-S2210650224002608-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151811","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}

引用次数: 0

Manifold-assisted coevolutionary algorithm for constrained multi-objective optimization 约束多目标优化的歧义辅助协同进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-30 DOI: 10.1016/j.swevo.2024.101717

{"title":"Manifold-assisted coevolutionary algorithm for constrained multi-objective optimization","authors":"","doi":"10.1016/j.swevo.2024.101717","DOIUrl":"10.1016/j.swevo.2024.101717","url":null,"abstract":"<div><p>In constrained multi-objective optimization problems (CMOPs), constraints often fragment the Pareto solution space into multiple feasible and infeasible regions. This fragmentation presents a challenge for evolutionary optimization methods as feasible regions can be discrete and isolated by infeasible areas, making exploration difficult and leading to populations getting trapped in local optima. To address these issues, this paper introduces a manifold assisted coevolutionary algorithm for solving CMOPs. Firstly, a guided feasible search strategy is proposed to explore feasible regions, especially those isolated by infeasible barriers. This is achieved by estimating directions to the Constrained Pareto Set (CPS). Secondly, a manifold learning-based exploration strategy is employed to spread the population along the Pareto Set (PS) manifold by estimating the manifold distribution. Moreover, two populations are exploited, where the first population serves as the primary population, considering both constraints and objectives to explore the feasible region and search along the CPS. The second population, on the other hand, does not consider constraints and serves as an auxiliary population to explore the Unconstrained PS. By cooperating, these two populations effectively approach and cover separated CPS segments. The proposed algorithm is evaluated against seven state-of-the-art algorithms on 37 CMOP test functions and 5 CMOPs with fraudulent constraints. The experimental results clearly demonstrate that our algorithm can reliably locate multiple CPSs and is considered state-of-the-art in handling CMOPs.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration 基于拥挤局部搜索的多群体遗传算法用于模糊多目标供应链配置

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-29 DOI: 10.1016/j.swevo.2024.101698

{"title":"Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration","authors":"","doi":"10.1016/j.swevo.2024.101698","DOIUrl":"10.1016/j.swevo.2024.101698","url":null,"abstract":"<div><p>Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing 深度强化学习辅助无人机记忆调度，用于铁路导轨除冰

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-29 DOI: 10.1016/j.swevo.2024.101719

{"title":"Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing","authors":"","doi":"10.1016/j.swevo.2024.101719","DOIUrl":"10.1016/j.swevo.2024.101719","url":null,"abstract":"<div><p>Icy rainfall and snowfall in 2024 Spring Festival struck the high-speed railway catenary systems and caused serious traffic disruptions in central and eastern China. Deicing drones are an effective method in response to these freezing events due to their fast speed and high environmental tolerance. However, the large disaster-affected area and the large scale and complexity of catenary networks make deicing drone scheduling a very difficult problem. In this paper, we formulate two versions of deicing drone scheduling problem, one for single drone scheduling and the other for multiple drone scheduling. Unlike most existing vehicle/drone routing problems, our problem aims to minimize the total negative effect caused by the freezing events on train operations, which reflects the prime concern of the decision-maker and is highly complex. To efficiently solve the problem, we propose a reinforcement learning assisted memetic optimization algorithm, which integrates global mutation and a set of neighborhood search operators adaptively selected by deep reinforcement learning. Computational results on real-world problem instances demonstrate its significant performance advantages over selected popular optimization algorithms in the literature.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective evolutionary neural architecture search for network intrusion detection 网络入侵检测的多目标进化神经架构搜索

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-29 DOI: 10.1016/j.swevo.2024.101702

{"title":"Multi-objective evolutionary neural architecture search for network intrusion detection","authors":"","doi":"10.1016/j.swevo.2024.101702","DOIUrl":"10.1016/j.swevo.2024.101702","url":null,"abstract":"<div><p>Network Intrusion Detection (NID) becomes significantly important for protecting the security of information systems, as the frequency and complexity of network attacks are increasing with the rapid development of the Internet. Recent research studies have proposed various neural network models for NID, but they need to manually design the network architectures based on expert knowledge, which is very time-consuming. To solve this problem, this paper proposes a Multi-objective Evolutionary Neural Architecture Search (MENAS) method, which can automatically design neural network models for NID. First, a comprehensive search space is designed and then a weight-sharing mechanism is used to construct a supernet for NID, allowing each subnet to inherit weights from the supernet for direct performance evaluation. Subsequently, the subnets are encoded as chromosomes for multi-objective evolutionary search, which simultaneously optimizes two objectives: enhancing the model’s detection performance and reducing its complexity. To improve the search capability, a path-based crossover method is designed, which can iteratively refine the subnets’ architectures by simultaneously optimizing their accuracy and complexity for NID. At last, our MENAS method has been validated through extensive experiments on three well-known NID datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The experiments show that our MENAS method obtains an average 1.45% improvement on accuracy and an average 68.70% reduction on floating-point operations through multi-objective optimization process on six scenarios, which outperforms some state-of-the-art NID methods.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CIR-DE: A chaotic individual regeneration mechanism for solving the stagnation problem in differential evolution CIR-DE：解决微分进化停滞问题的混沌个体再生机制

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-28 DOI: 10.1016/j.swevo.2024.101718

{"title":"CIR-DE: A chaotic individual regeneration mechanism for solving the stagnation problem in differential evolution","authors":"","doi":"10.1016/j.swevo.2024.101718","DOIUrl":"10.1016/j.swevo.2024.101718","url":null,"abstract":"<div><p>Stagnant evolution is a problem frequently encountered by the population in differential evolution (DE). Aiming at the stagnation phenomenon, a comprehensive interpretation is provided in this paper. Our experiment confirms that the individuals that continuously stop evolving can be classified into two categories: global and local stagnant individuals, whose causes and exhibited characteristics are associated with the search behavior of the population. Based on the above findings, we propose a chaotic individual regeneration framework (CIR) for DEs. In the CIR-DE, a monitor is designed to recognize different types of stagnant individuals by evaluating the whole population’s convergence speed and specific individual’s location. Besides, two chaotic regeneration techniques are proposed to guide the above two types of individuals away from stagnation using the knowledge from solution and objective spaces. The CIR framework is implemented in nine representative DEs and tested in the CEC 2014, CEC 2017, CEC 2022 theoretical benchmarks and five real-world problems. The results reveal that our framework can significantly improve original DEs’ performance and alleviate stagnation in both theoretical and practical scenarios. The CIR framework also shows strong competitiveness compared to the other stagnation-related frameworks and the state-of-the-art DE variants.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring interpretable evolutionary optimization via significance of each constraint and population diversity 通过各约束条件的重要性和种群多样性探索可解释的进化优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-28 DOI: 10.1016/j.swevo.2024.101679

{"title":"Exploring interpretable evolutionary optimization via significance of each constraint and population diversity","authors":"","doi":"10.1016/j.swevo.2024.101679","DOIUrl":"10.1016/j.swevo.2024.101679","url":null,"abstract":"<div><p>Evolutionary algorithms (EAs) have been widely employed to solve complex constrained optimization problems (COPs). However, numerous EAs treat constraints as a collective black box, employing a uniform processing technique for all constraints. Generally, there exists variability in the significance of each constraint within COPs. To address this issue, this paper is the first attempt to investigate the significance of each constraint spontaneously during the evolution process, and then proposes a co-directed evolutionary algorithm (CdEA-SCPD) for exploring interpretable COPs. First, CdEA-SCPD develops an adaptive penalty function designed to assign different weights to constraints based on their violation severity, thereby varying the significance of each constraint to enhance interpretability and facilitate the algorithm to converge more rapidly toward the global optimum. In addition, a dynamic archiving strategy and a shared replacement mechanism are developed to improve the population diversity of CdEA-SCPD. Extensive experiments on benchmark functions from IEEE CEC2006, CEC2010, and CEC2017 and three engineering problems demonstrate the superiority of the proposed CdEA-SCPD compared to existing competitive EAs. Specifically, on the benchmark functions from IEEE CEC2010, the proposed method yields <span><math><mi>ρ</mi></math></span> values lower than 0.05 in the multiple-problem Wilcoxon's signed rank test and ranks first in the Friedman's test. Furthermore, ablation analysis and parameter analysis have demonstrated the beneficial effects of the proposed strategies.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0