Swarm and Evolutionary Computation最新文献

{"title":"GSRPSO: A multi-strategy integrated particle swarm algorithm for multi-threshold segmentation of real cervical cancer images","authors":"Gang Hu ,&nbsp;Yixuan Zheng ,&nbsp;Essam H. Houssein ,&nbsp;Guo Wei","doi":"10.1016/j.swevo.2024.101766","DOIUrl":"10.1016/j.swevo.2024.101766","url":null,"abstract":"<div><div>Cervical cancer is one of the most common gynecological malignant tumors and the fourth largest malignant tumor endangering the life and health of women worldwide. The lesion areas of cervical cancer usually show complexity and diversity. To distinguish different cervical lesion sites and assist doctors in diagnosis and decision-making, this paper first proposes a multi-strategy integrated particle swarm optimization algorithm (GSRPSO, for short). The four strategies in GSRPSO work together to improve its optimization capabilities. Among them, the dynamic parameters balance the exploration and exploitation phases. The gaining sharing strategy and the random position updating strategy accelerate the convergence process while enhancing the diversity of the population. The vertical crossover mutation strategy improves local exploitation and avoids premature stagnation of the algorithm. The optimization performance of GSRPSO is validated by comparison experiments with 15 state-of-the-art algorithms on the CEC2020 test set. In addition, we established a MIS method based on the GSRPSO algorithm by combining the non-local mean algorithm and 2D Kapur entropy. This method is compared with the MIS methods combining 2D Renyi entropy, 2D Tsallis entropy, and 2D Masi entropy in a comparison experiment with four sets of thresholds on six BSDS500 images. The experimental results show that this MIS method exhibits obvious advantages in segmentation quality and stability. Finally, nine cervical cancer images are segmented using the GSRPSO-based MIS method, and experiments are conducted with nine excellent optimization algorithms under six different sets of thresholds. The experimental results show that this MIS method demonstrates better segmentation quality and accuracy than similar methods with the optimal evaluation indexes PSNR=28.3645, SSIM=0.8996, FSIM=0.9494, AD=8.1939, and NAE=0.0710. In conclusion, the GSRPSO-based MIS method is one of a class of highly promising methods to assist physicians in accurately diagnosing cervical cancer.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101766"},"PeriodicalIF":8.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554456","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":"ERLNEIL-MDP: Evolutionary reinforcement learning with novelty-driven exploration for medical data processing","authors":"Jianhui Lv ,&nbsp;Byung-Gyu Kim ,&nbsp;Adam Slowik ,&nbsp;B.D. Parameshachari ,&nbsp;Saru Kumari ,&nbsp;Chien-Ming Chen ,&nbsp;Keqin Li","doi":"10.1016/j.swevo.2024.101769","DOIUrl":"10.1016/j.swevo.2024.101769","url":null,"abstract":"<div><div>The rapid growth of medical data presents opportunities and challenges for healthcare professionals and researchers. To effectively process and analyze this complex and heterogeneous data, we propose evolutionary reinforcement learning with novelty-driven exploration and imitation learning for medical data processing (ERLNEIL-MDP) algorithm, including a novelty computation mechanism, an adaptive novelty-fitness selection strategy, an imitation-guided experience fusion mechanism, and an adaptive stability preservation module. The novelty computation mechanism quantifies the novelty of each policy based on its dissimilarity to the population and historical data, promoting exploration and diversity. The adaptive novelty-fitness selection strategy balances exploration and exploitation by considering policies' novelty and fitness during selection. The imitation-guided experience fusion mechanism incorporates expert knowledge and demonstrations into the learning process, accelerating the discovery of effective solutions. The adaptive stability preservation module ensures the stability and reliability of the learning process by dynamically adjusting the algorithm's hyperparameters and preserving elite policies across generations. These components work together to enhance the exploration, diversity, and stability of the learning process. The significance of this work lies in its potential to revolutionize medical data analysis, leading to more accurate diagnoses and personalized treatments. Experiments on MIMIC-III and n2c2 datasets demonstrate ERLNEIL-MDP's superior performance, achieving F1 scores of 0.933 and 0.928, respectively, representing 6.0 % and 6.7 % improvements over state-of-the-art methods. The algorithm exhibits strong convergence, high population diversity, and robustness to noise and missing data.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101769"},"PeriodicalIF":8.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554455","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":"Efficient ontology matching through compact linear genetic programming with surrogate-assisted local search","authors":"Xingsi Xue ,&nbsp;Jerry Chun-Wei Lin ,&nbsp;Tong Su","doi":"10.1016/j.swevo.2024.101758","DOIUrl":"10.1016/j.swevo.2024.101758","url":null,"abstract":"<div><div>Ontology is a foundational technique of Semantic Web, which enables meaningful interpretation of Web data. However, ontology heterogeneity obstructs the communications among different ontologies, which is a key hindrance in realizing Semantic Web. To leverage different ontologies, it is important to match ontologies by identifying their semantically related entities. Given the vast number of entities and rich vocabulary semantics, this task presents considerable challenges. To tackle this challenge, this paper proposes a novel Compact Linear Genetic Programming with Surrogate-Assisted Local Search (CLGP-SALS). First, a compact multi-program encoding mechanism is developed to reduce the computational cost while ensuring the reusability of building blocks in Linear Genetic Programming. Moreover, it coordinates multiple programs within one solution to improve the quality of ontology alignment. Second, to enhance convergence speed, a new Surrogate-Assisted Local Search is designed, incorporating semantic distance and fitness discrepancies for a focused local search process. The surrogate model presents a superior approach for approximating the fitness of individuals, thereby improving search efficiency in the ontology matching task. Experimental results demonstrate that CLGP-SALS outperforms the state-of-the-art ontology matching methods on the ontology alignment evaluation initiative’s benchmark. The results show that our method can efficiently determine high-quality ontology alignments, and its performance outperforms the compared methods in terms of both effectiveness and efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101758"},"PeriodicalIF":8.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538753","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":"DQL-assisted competitive evolutionary algorithm for energy-aware robust flexible job shop scheduling under unexpected disruptions","authors":"Shicun Zhao ,&nbsp;Hong Zhou ,&nbsp;Yujie Zhao ,&nbsp;Da Wang","doi":"10.1016/j.swevo.2024.101750","DOIUrl":"10.1016/j.swevo.2024.101750","url":null,"abstract":"<div><div>Energy-aware scheduling has emerged as a well-defined research topic for achieving sustainable development. However, unexpected disruptions in workshop pose several challenges to the manufacturing process, causing the original schedules to become non-optimal or even infeasible, resulting in significant energy waste. Consequently, it is necessary to investigate the robust scheduling problem with energy-awareness. The energy-efficient robust flexible job shop scheduling problem (ERFJSP) aims to simultaneously optimize scheduling efficiency, total energy consumption, and robustness. A two-stage mixed-integer linear programming model considering machine breakdown and job insertion is formulated in this paper for the first time. To solve this problem, a double Q-learning (DQL)-assisted competitive evolutionary algorithm (DQCEA) is proposed. In DQCEA, a heuristic initialization strategy is first designed, which allows it to obtain high-quality and diverse solutions. Subsequently, a multi-objective competitive mechanism is proposed to classify search individuals into inferiors and superiors. Moreover, an inferior-pushing and superior-pulling-based crossover is designed for inferior members, facilitating knowledge transfer within the population and enhancing global diversification capability. Meanwhile, a hyper-mutation operator is devised for superior members, which incorporates eight search strategies to improve local intensification ability. Furthermore, DQL is employed to learn and recommend the most suitable strategy for each superior individual. Finally, extensive experiments are carried out to evaluate the correctness of the formulated MILP model and the performance of DQCEA. Experimental analysis confirms that DQCEA effectively provides promising schedules under different scenarios, demonstrating its reliability in addressing unexpected disruption challenges.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101750"},"PeriodicalIF":8.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538755","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 tri-chromosome-based evolutionary algorithm for energy-efficient workflow scheduling in clouds","authors":"Yangkun Xia ,&nbsp;Xinran Luo ,&nbsp;Ting Jin ,&nbsp;Jun Li ,&nbsp;Lining Xing","doi":"10.1016/j.swevo.2024.101751","DOIUrl":"10.1016/j.swevo.2024.101751","url":null,"abstract":"<div><div>Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101751"},"PeriodicalIF":8.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538742","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 population hierarchical-based evolutionary algorithm for large-scale many-objective optimization","authors":"Shiting Wang ,&nbsp;Jinhua Zheng ,&nbsp;Yingjie Zou ,&nbsp;Yuan Liu ,&nbsp;Juan Zou ,&nbsp;Shengxiang Yang","doi":"10.1016/j.swevo.2024.101752","DOIUrl":"10.1016/j.swevo.2024.101752","url":null,"abstract":"<div><div>In large-scale many-objective optimization problems (LMaOPs), the performance of algorithms faces significant challenges as the number of objective functions and decision variables increases. The main challenges in addressing this type of problem are as follows: the large number of decision variables creates an enormous decision space that needs to be explored, leading to slow convergence; and the high-dimensional objective space presents difficulties in selecting dominant individuals within the population. To address this issue, this paper introduces an evolutionary algorithm based on population hierarchy to address LMaOPs. The algorithm employs different strategies for offspring generation at various population levels. Initially, the population is categorized into three levels by fitness value: poorly performing solutions with higher fitness (<span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>h</mi></mrow></msub></math></span>), better solutions with lower fitness (<span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>l</mi></mrow></msub></math></span>), and excellent individuals stored in the archive set (<span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>a</mi></mrow></msub></math></span>). Subsequently, a hierarchical knowledge integration strategy (HKI) guides the evolution of individuals at different levels. Individuals in <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>l</mi></mrow></msub></math></span> generate offspring by integrating differential knowledge from <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>a</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>h</mi></mrow></msub></math></span>, while individuals in <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>h</mi></mrow></msub></math></span> generate offspring by learning prior knowledge from <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>a</mi></mrow></msub></math></span>. Finally, using a cluster-based environment selection strategy balances population diversity and convergence. Extensive experiments on LMaOPs with up to 10 objectives and 5000 decision variables validate the algorithm’s effectiveness, demonstrating superior performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101752"},"PeriodicalIF":8.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538740","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":"Open shop scheduling with group and transportation operations by learning-driven hyper-heuristic algorithms","authors":"Yifeng Wang ,&nbsp;Yaping Fu ,&nbsp;Kaizhou Gao ,&nbsp;Humyun Fuad Rahman ,&nbsp;Min Huang","doi":"10.1016/j.swevo.2024.101757","DOIUrl":"10.1016/j.swevo.2024.101757","url":null,"abstract":"<div><div>Open shop scheduling problems (OSSPs) are complex scheduling problems, which have been extensively studied in the literature. Group and transportation activities are two important aspects of OSSPs that still need attention. This work considers an OSSP with group and transportation operations to minimize maximum completion time by solving three key sub-problems: job assignment among groups, job sequence in groups and group sequence on machines. Firstly, an integer programming model is formulized to define the problem. Secondly, a learning-driven hyper-heuristic algorithm is developed by incorporating a Q-learning method and four meta-heuristics, i.e., genetic algorithm, artificial bee colony optimization, variable neighborhood search method and Jaya algorithm. The Q-learning method is devised to select the most promising meta-heuristic for performing at each iteration. Three neighborhood structures are designed by integrating critical machines and critical paths. Finally, the developed model is verified by an exact solver CPLEX, and the comparison results exhibit that CPLEX is effective for instances with ten jobs. For the instances with more than ten jobs, the developed algorithm wins CPLEX in terms of computation accuracy and efficiency, signifying its excellent performance in finding better solutions. Furthermore, four meta-heuristics mentioned above and three state-of-the-art meta-heuristics are employed for comparisons in solving a set of benchmark test instances. The results confirm that the formulated model and algorithm have stronger competitiveness in handling the considered problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101757"},"PeriodicalIF":8.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538741","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 memory usage guided fuzzing based on particle swarm optimization","authors":"Shengran Wang ,&nbsp;Jinfu Chen ,&nbsp;Jianming Zhang ,&nbsp;Saihua Cai ,&nbsp;Wen Zhang ,&nbsp;Jian Sun","doi":"10.1016/j.swevo.2024.101747","DOIUrl":"10.1016/j.swevo.2024.101747","url":null,"abstract":"<div><div>Fuzzing has become the focus of research in the field of software testing due to its advantages such as simple testing process, easy deployment, and easy reproduction of crashes. However, fuzzing also suffers from the disadvantages of poor test purpose and invalid generated seeds. To solve the above problems, researchers proposed the Memory Usage Guided Fuzzing (MUGF). To further optimize the performance of MUGF, this paper proposes a fuzzing method named Memory Usage Guided Fuzzing based on Particle Swarm Optimization (MUGF-PSO). MUGF-PSO will guide the selection of mutation operators for subsequent testing by learning the effectiveness of each mutation operators during previous testing. Specifically, MUGF-PSO regards each mutation operator as a particle in the particle swarm optimization algorithm, and the coverage change is regarded as an important factor to evaluate the seed’s local and global best position. In addition, the efficient selection probability distribution of mutation operator is constantly iteratively searched to make the MUGF tends to select the mutation operator that is more able to trigger new coverage. Furthermore, the MUGF-PSO is integrated into MemLock developed by MUGF, that is, MemLock-PSO is developed. We conduct a comparison experiment with 5 fuzzers (including AFL, MemLock, EcoFuzz, HavocMAB and Darwin), and the results show that MemLock-PSO is able to find more paths and crashes in 12 widely used program with different functions from 10 tools, with a significant gap of around 12 h. Meanwhile, the MemLock-PSO have a significant difference in the number of paths and crashes compared to other fuzzers with a good stability. Our work also proves the remarkable significance of applying swarm intelligence optimization algorithms in fuzzing in order to solve the problem of selection of mutation operators.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101747"},"PeriodicalIF":8.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444953","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 cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach","authors":"Thao Nguyen Da ,&nbsp;Phuong Nguyen Thanh ,&nbsp;Ming-Yuan Cho","doi":"10.1016/j.swevo.2024.101755","DOIUrl":"10.1016/j.swevo.2024.101755","url":null,"abstract":"<div><div>Real-time insulator leakage current classification is crucial in preventing the pollution flashover phenomenon and providing appropriate maintenance schedules in high-voltage transmission towers. However, current methodologies only utilize traditional artificial neural networks, which have limitations when performing big data analysis. This research developed a novel cloud 15kV-HDPE insulator leakage current classified framework, utilizing a long short-term memory convolutional neural network (LSTM-CNN). The hybrid model structure is optimized through hyperparameter fine-tuning based on improved particle swarm optimization (IPSO), which reduces human effort and considerable time compared with PSO and random search (RS) techniques. The IPSO-LSTM-CNN model can productively identify correlations between selected weather features and target leakage current levels of 15kV-HDPE insulators. LSTM efficiently captures long-term patterns in sequential data, while CNN layers competently extract high-level dependency in time-invariant information. Four 15kV-HDPE insulators’ datasets, collected in high-voltage transmission lines in the coastal area of Taiwan for more than one year, are deployed for analyzing and comparing classified performance. Other conventional models are developed to evaluate and compare classified performance with the proposed IPSO-LSTM-CNN approach, which acquires the most significant enhancement of 48.08 % loss, 45.91 % validating loss, 52.57 % MAE, 35.47 % validating MAE, 47.34 % MSE, 27.02 % validating MSE, 9.15 % PRE, 3.40 % validating PRE, 4.76 % REC, and 6.17 % validating REC. The experiment outcomes demonstrate that the developed IPSO-LSTM-CNN model acquires improved robustness and accuracy in the leakage current classified capability of 15kV-HDPE insulators.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101755"},"PeriodicalIF":8.2,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432957","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 multi-strategy optimizer for energy minimization of multi-UAV-assisted mobile edge computing","authors":"Yang Chen ,&nbsp;Dechang Pi ,&nbsp;Shengxiang Yang ,&nbsp;Yue Xu ,&nbsp;Bi Wang ,&nbsp;Yintong Wang","doi":"10.1016/j.swevo.2024.101748","DOIUrl":"10.1016/j.swevo.2024.101748","url":null,"abstract":"<div><div>Disasters in remote areas often cause damage to communication facilities, which presents significant challenges for rescue efforts. As flexible mobile devices, unmanned aerial vehicles (UAVs) can provide temporary network services to address this issue. This paper studies the use of UAVs as mobile base stations to offer offload computing services for disaster relief devices in affected areas. To ensure reliable communication between disaster relief devices and UAVs, we construct a multi-UAV-assisted mobile edge computing (MEC) system with the objective of minimizing system energy consumption. Inspired by swarm intelligence principles, we propose a multi-strategy optimizer (MSO) that defines various population search functions and employs superior neighborhood methods for population updates. Experimental results demonstrate that MSO achieves superior system energy efficiency and exhibits greater stability compared to several state-of-the-art swarm intelligence algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101748"},"PeriodicalIF":8.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424804","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}