This study analyzes the literature and reviews the trends and development trajectories of Particle Swarm Optimization (PSO)-based scheduling. Main Path and Cluster Analysis identify the seminal features introduced to improve PSO, and the major application areas. This serves as the basis for discussing computational advancements. The findings suggest that PSO is most developed in flow-shop scheduling, with its evolution progressing from single- to multi-objective optimization. The main application has shifted from production to advanced computing and energy management, indicating the growing influence of AI, renewables and energy storage. The shift towards mass customization explains the projected growth of flexible job-shop scheduling.
{"title":"Tracing the evolution of Particle Swarm Optimization in scheduling: A systematic review using main path analysis","authors":"Kuo-Ching Ying , Pourya Pourhejazy , Kuan-Lun Huang","doi":"10.1016/j.swevo.2025.102239","DOIUrl":"10.1016/j.swevo.2025.102239","url":null,"abstract":"<div><div>This study analyzes the literature and reviews the trends and development trajectories of Particle Swarm Optimization (PSO)-based scheduling. Main Path and Cluster Analysis identify the seminal features introduced to improve PSO, and the major application areas. This serves as the basis for discussing computational advancements. The findings suggest that PSO is most developed in flow-shop scheduling, with its evolution progressing from single- to multi-objective optimization. The main application has shifted from production to advanced computing and energy management, indicating the growing influence of AI, renewables and energy storage. The shift towards mass customization explains the projected growth of flexible job-shop scheduling.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102239"},"PeriodicalIF":8.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684855","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}
Pub Date : 2025-12-04DOI: 10.1016/j.swevo.2025.102244
Anran Cao , Xiaoli Li , Kang Wang
Wet flue gas desulfurization (WFGD) process is crucial for reducing SO emissions in coal-fired power plants. In this process, the limestone slurry reacts with the SO emissions. Meanwhile, the reaction efficiency is sped up by pumps and oxidation fan, which is positively correlated with their electrical load. Herein, three conflicting objectives need to be minimized: SO emissions, electrical load, and use of limestone slurry. Meanwhile, many uncontrollable factors change over time, resulting in rapidly or random changing Pareto-optimal solution set (PS), i.e., a dynamic environment. Therefore, we formulate WFGD problem as a dynamic multi-objective optimization problem (DMOP). To solve WFGD problem, a simple yet effective algorithm, a dynamic multi-objective optimization evolutionary strategy based on adaptive selection (ASS), is proposed in this paper. When a change occurs, ASS provides diversified solutions by different proposed strategies, namely, a diverse direction prediction and a center-guided self-correcting prediction. Based on the severity of environmental change, an adaptive selection mechanism can adjust the selection probability of each strategy. ASS consists of two different prediction strategies, enabling more responsive to different changes. Comprehensive empirical studies shows that ASS achieves an excellent performance on CEC2018 DMOP benchmarks and well solve real-world WFGD problem compared to four state-of-the-art algorithms.
{"title":"A dynamic multi-objective optimization evolutionary strategy based on adaptive selection for wet flue gas desulfurization process","authors":"Anran Cao , Xiaoli Li , Kang Wang","doi":"10.1016/j.swevo.2025.102244","DOIUrl":"10.1016/j.swevo.2025.102244","url":null,"abstract":"<div><div>Wet flue gas desulfurization (WFGD) process is crucial for reducing SO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions in coal-fired power plants. In this process, the limestone slurry reacts with the SO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. Meanwhile, the reaction efficiency is sped up by pumps and oxidation fan, which is positively correlated with their electrical load. Herein, three conflicting objectives need to be minimized: SO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions, electrical load, and use of limestone slurry. Meanwhile, many uncontrollable factors change over time, resulting in rapidly or random changing Pareto-optimal solution set (PS), i.e., a dynamic environment. Therefore, we formulate WFGD problem as a dynamic multi-objective optimization problem (DMOP). To solve WFGD problem, a simple yet effective algorithm, a dynamic multi-objective optimization evolutionary strategy based on adaptive selection (ASS), is proposed in this paper. When a change occurs, ASS provides diversified solutions by different proposed strategies, namely, a diverse direction prediction and a center-guided self-correcting prediction. Based on the severity of environmental change, an adaptive selection mechanism can adjust the selection probability of each strategy. ASS consists of two different prediction strategies, enabling more responsive to different changes. Comprehensive empirical studies shows that ASS achieves an excellent performance on CEC2018 DMOP benchmarks and well solve real-world WFGD problem compared to four state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102244"},"PeriodicalIF":8.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684935","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}
Pub Date : 2025-12-04DOI: 10.1016/j.swevo.2025.102243
Shi Wu , Hu Peng , Zhuo Liu , Lin Liu , Lianglin Cao , Zhijian Wu
Resource constraints exist in solving realistic dynamic multi-objective optimization problems (DMOPs), such as those based on low-power microprocessors. However, traditional dynamic multi-objective evolutionary algorithms (DMOEAs) often rely on large populations, and running these algorithms directly on low-power microprocessors will result in program interruptions. In contrast, the micro population can effectively balance limited computing resources and computational efficiency, making it an effective approach for running DMOEAs on low-power microprocessors. In light of this analysis, a micro dynamic multi-objective evolutionary algorithm with flexible response strategy (DMOEA-FRS) is proposed. This approach incorporates a flexible niche strategy to maximize information exchange among niches, identifying the current environment based on niche performance and updating relevant information accordingly. Subsequently, the static optimization phase determines the most suitable environmental selection method based on insights obtained from the dynamic response phase. This strategy significantly enhances the adaptability of the micro population in dynamic environments. Additionally, a flexible scaling mechanism is introduced, which improves the diversity of the algorithm, facilitates the exploration of new regions within the solution space, and balances convergence with diversity. The performance of DMOEA-FRS is compared against eight state-of-the-art DMOEAs across 35 test instances, demonstrating superior results in most cases. Furthermore, for the application to real-world problems, the algorithm was simulated within a small-scale smart greenhouse equipped with a low-power microprocessor. The results confirm the feasibility of DMOEA-FRS for optimization within low-power microprocessor environments.
{"title":"A micro dynamic multi-objective evolutionary algorithm with flexible response strategy","authors":"Shi Wu , Hu Peng , Zhuo Liu , Lin Liu , Lianglin Cao , Zhijian Wu","doi":"10.1016/j.swevo.2025.102243","DOIUrl":"10.1016/j.swevo.2025.102243","url":null,"abstract":"<div><div>Resource constraints exist in solving realistic dynamic multi-objective optimization problems (DMOPs), such as those based on low-power microprocessors. However, traditional dynamic multi-objective evolutionary algorithms (DMOEAs) often rely on large populations, and running these algorithms directly on low-power microprocessors will result in program interruptions. In contrast, the micro population can effectively balance limited computing resources and computational efficiency, making it an effective approach for running DMOEAs on low-power microprocessors. In light of this analysis, a micro dynamic multi-objective evolutionary algorithm with flexible response strategy (<span><math><mi>μ</mi></math></span>DMOEA-FRS) is proposed. This approach incorporates a flexible niche strategy to maximize information exchange among niches, identifying the current environment based on niche performance and updating relevant information accordingly. Subsequently, the static optimization phase determines the most suitable environmental selection method based on insights obtained from the dynamic response phase. This strategy significantly enhances the adaptability of the micro population in dynamic environments. Additionally, a flexible scaling mechanism is introduced, which improves the diversity of the algorithm, facilitates the exploration of new regions within the solution space, and balances convergence with diversity. The performance of <span><math><mi>μ</mi></math></span>DMOEA-FRS is compared against eight state-of-the-art DMOEAs across 35 test instances, demonstrating superior results in most cases. Furthermore, for the application to real-world problems, the algorithm was simulated within a small-scale smart greenhouse equipped with a low-power microprocessor. The results confirm the feasibility of <span><math><mi>μ</mi></math></span>DMOEA-FRS for optimization within low-power microprocessor environments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102243"},"PeriodicalIF":8.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684936","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}
Pub Date : 2025-12-04DOI: 10.1016/j.swevo.2025.102247
Junpeng Chen , Zhenyu Meng
Most Differential Evolution (DE) researchers tend to adopt the binomial crossover operation in tackling optimization problems. However, we find that the DE variants using exponential crossover can also achieve superior performance to those using binomial crossover, as long as appropriate parameter control strategies are applied. Therefore, this paper proposes a new DE algorithm, an adaptive Differential Evolution algorithm with exponential crossover based on Learning Strategy within Difference vector (DLS-DE), to fill the gap in this field. The main contributions of this work are summarized as follows: First, a two-phase parameter control strategy is designed to regulate the scale factor for balancing exploration and exploitation. In addition, considering the dispersion of effective parameter values, an adaptive strategy is proposed to adjust the sampling distribution and enhance parameter adaptability. Second, a differential vector learning strategy is developed to identify and incorporate promising difference vector information during an individual’s stagnation, enabling the search direction to adapt based on its past performance. Finally, the algorithm employs exponential crossover, where the crossover rate is automatically generated, and a fitness-independent parameter weight update mechanism is adopted to mitigate premature convergence. The performance of DLS-DE is evaluated on 88 benchmark functions from the CEC2013, CEC2014, and CEC2017 test suites. Statistical analyses, including the Friedman test and the Wilcoxon rank-sum test, demonstrate its effectiveness and competitiveness compared with ten state-of-the-art algorithms. In addition, DLS-DE is applied to an Economic Load Dispatch (ELD) problem in a power system with 40 generating units, achieving satisfactory results.
{"title":"An adaptive differential evolution algorithm with exponential crossover based on a learning strategy within the difference vector","authors":"Junpeng Chen , Zhenyu Meng","doi":"10.1016/j.swevo.2025.102247","DOIUrl":"10.1016/j.swevo.2025.102247","url":null,"abstract":"<div><div>Most Differential Evolution (DE) researchers tend to adopt the binomial crossover operation in tackling optimization problems. However, we find that the DE variants using exponential crossover can also achieve superior performance to those using binomial crossover, as long as appropriate parameter control strategies are applied. Therefore, this paper proposes a new DE algorithm, an adaptive Differential Evolution algorithm with exponential crossover based on Learning Strategy within Difference vector (DLS-DE), to fill the gap in this field. The main contributions of this work are summarized as follows: First, a two-phase parameter control strategy is designed to regulate the scale factor <span><math><mi>F</mi></math></span> for balancing exploration and exploitation. In addition, considering the dispersion of effective parameter values, an adaptive <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mi>F</mi></mrow></msub></math></span> strategy is proposed to adjust the sampling distribution and enhance parameter adaptability. Second, a differential vector learning strategy is developed to identify and incorporate promising difference vector information during an individual’s stagnation, enabling the search direction to adapt based on its past performance. Finally, the algorithm employs exponential crossover, where the crossover rate <span><math><mrow><mi>C</mi><mi>R</mi></mrow></math></span> is automatically generated, and a fitness-independent parameter weight update mechanism is adopted to mitigate premature convergence. The performance of DLS-DE is evaluated on 88 benchmark functions from the CEC2013, CEC2014, and CEC2017 test suites. Statistical analyses, including the Friedman test and the Wilcoxon rank-sum test, demonstrate its effectiveness and competitiveness compared with ten state-of-the-art algorithms. In addition, DLS-DE is applied to an Economic Load Dispatch (ELD) problem in a power system with 40 generating units, achieving satisfactory results.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102247"},"PeriodicalIF":8.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684934","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}
The air-ground cross-domain unmanned swarm is widely used in military reconnaissance, disaster rescue and other fields, but its path planning faces severe challenges such as heterogeneous swarm coordination and highly dynamic obstacle avoidance in complex dynamic environments. Aiming at these technical difficulties, this paper proposes a target-guided adaptive path planning method (TAPP) inspired by the running behaviour of wolf packs in nature. The method adopts a parallel graph search strategy, dynamically fuses target location information to guide individual path planning, and simulates the autonomous decision-making mechanism of individual wolves. A local avoidance mechanism based on dynamic prioritization is also introduced to avoid motion conflicts while ensuring the efficiency of the swarm task. In order to comprehensively evaluate the effectiveness of the method, a complex air-ground cross-domain cooperative simulation scenario is designed, and systematic comparison experiments between the proposed method and several advanced path planning algorithms are conducted. The experimental data show that the proposed method demonstrates significant advantages in computational efficiency, path optimization, and adaptability, especially when dealing with large-scale and highly dynamic unmanned swarm tasks. The proposed method provides a solution for unmanned swarm path planning, which has a good application prospect.
{"title":"A target-guided adaptive path planning method for air-ground cross-domain unmanned swarm","authors":"Qiang Peng , Husheng Wu , Renjun Zhan , Jingyi Geng , Yuanqing Xia , Lining Xing","doi":"10.1016/j.swevo.2025.102220","DOIUrl":"10.1016/j.swevo.2025.102220","url":null,"abstract":"<div><div>The air-ground cross-domain unmanned swarm is widely used in military reconnaissance, disaster rescue and other fields, but its path planning faces severe challenges such as heterogeneous swarm coordination and highly dynamic obstacle avoidance in complex dynamic environments. Aiming at these technical difficulties, this paper proposes a target-guided adaptive path planning method (TAPP) inspired by the running behaviour of wolf packs in nature. The method adopts a parallel graph search strategy, dynamically fuses target location information to guide individual path planning, and simulates the autonomous decision-making mechanism of individual wolves. A local avoidance mechanism based on dynamic prioritization is also introduced to avoid motion conflicts while ensuring the efficiency of the swarm task. In order to comprehensively evaluate the effectiveness of the method, a complex air-ground cross-domain cooperative simulation scenario is designed, and systematic comparison experiments between the proposed method and several advanced path planning algorithms are conducted. The experimental data show that the proposed method demonstrates significant advantages in computational efficiency, path optimization, and adaptability, especially when dealing with large-scale and highly dynamic unmanned swarm tasks. The proposed method provides a solution for unmanned swarm path planning, which has a good application prospect.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102220"},"PeriodicalIF":8.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684856","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}
Pub Date : 2025-12-03DOI: 10.1016/j.swevo.2025.102249
Yongming Han , Fan Yu , Jiaxin Liu , Zhen Zhang , Bo Ma , Ling Wang , Zhiqiang Geng
Food safety is a major issue related to people's livelihood, which constantly affects human health. Currently, the effectiveness of many promising food safety risk evaluation methods is strongly depended on the static hyperparameter configuration due to complexity and diversity of hyperparameters. Therefore, this paper proposes a novel extreme gradient boosting (XGBOOST) classification method based on the improved tuna swarm optimization (ITSO) algorithm incorporating a dynamic adaptive mechanism to evaluate the food risk. The ITSO optimizes the initial population using an elite reverse learning strategy to prevent premature convergence. Meanwhile, a two-stage Nnonlinear function is designed to adjust the probability of spiral foraging strategy and parabolic foraging strategy, which makes the ITSO quickly explore the global search spatial in the early stage, and focus on local search spatial in the later stage. Furthermore, the Lévy Flight strategy is used to improve the abilities of searching and jumping out of the local optimum. Finally, the hybrid ITSO-XGBOOST is constructed, and the ITSO is used to adaptive optimizing the parameters of the XGBOOST to improve the food risk evaluation ability. Compared with traditional optimization algorithms, the ITSO demonstrates its superiority on seven different functions. And the experiments on a real-world multi-indicator dairy dataset verifies the proposed method. Specifically, compared with baselines, all evaluation indexes show the significant advantages of the ITSO-XGBOOST with the highest accuracy of 82.61 % and the F1 of 82.09 %, which fully verifies the stably and accurately accessing ability of the proposed method in the complex scenario of multiple indexes.
{"title":"An Improved Tuna Swarm Optimization Algorithm based XGBOOST Classification Method for Food Risk Evaluation","authors":"Yongming Han , Fan Yu , Jiaxin Liu , Zhen Zhang , Bo Ma , Ling Wang , Zhiqiang Geng","doi":"10.1016/j.swevo.2025.102249","DOIUrl":"10.1016/j.swevo.2025.102249","url":null,"abstract":"<div><div>Food safety is a major issue related to people's livelihood, which constantly affects human health. Currently, the effectiveness of many promising food safety risk evaluation methods is strongly depended on the static hyperparameter configuration due to complexity and diversity of hyperparameters. Therefore, this paper proposes a novel extreme gradient boosting (XGBOOST) classification method based on the improved tuna swarm optimization (ITSO) algorithm incorporating a dynamic adaptive mechanism to evaluate the food risk. The ITSO optimizes the initial population using an elite reverse learning strategy to prevent premature convergence. Meanwhile, a two-stage Nnonlinear function is designed to adjust the probability of spiral foraging strategy and parabolic foraging strategy, which makes the ITSO quickly explore the global search spatial in the early stage, and focus on local search spatial in the later stage. Furthermore, the Lévy Flight strategy is used to improve the abilities of searching and jumping out of the local optimum. Finally, the hybrid ITSO-XGBOOST is constructed, and the ITSO is used to adaptive optimizing the parameters of the XGBOOST to improve the food risk evaluation ability. Compared with traditional optimization algorithms, the ITSO demonstrates its superiority on seven different functions. And the experiments on a real-world multi-indicator dairy dataset verifies the proposed method. Specifically, compared with baselines, all evaluation indexes show the significant advantages of the ITSO-XGBOOST with the highest accuracy of 82.61 % and the F1 of 82.09 %, which fully verifies the stably and accurately accessing ability of the proposed method in the complex scenario of multiple indexes.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102249"},"PeriodicalIF":8.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736996","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}
Pub Date : 2025-12-03DOI: 10.1016/j.swevo.2025.102238
Libin Hong , Dongxu Zhang , Tianxiang Cui
The United Multi-Operator Evolutionary Algorithms (UMOEAs) demonstrate high performance in evolutionary computation due to their combination of various operators for Differential Evolution (DE) variants and local search mechanisms, and have evolved into three versions over the last decade. In this work, a novel variant of the UMOEAs is proposed, named UMOEAs-IV. UMOEAs-IV employs a novel calculation method for the scaling factor , which generates dynamic values to adaptively control the step size of mutation operators, a mutation strategy with complementary operators that better balance exploration and exploitation, an Estimation-of-Distribution Algorithm (EDA) to learn the probabilistic distribution of promising individuals, and a stagnation strategy to help individuals escape local optima. UMOEAs-IV is compared with 15 recently proposed DE-based algorithms and tested on the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark functions, showing superior performance over all of them. It is also applied to livestock feed ration optimization for beef and dairy cattle, where it shows top performance in the experimental results. The source code of UMOEAs-IV is provided at https://github.com/microhard1999/CODES.
{"title":"A variant of united multi-operator evolutionary algorithms with application to livestock feed ration optimization","authors":"Libin Hong , Dongxu Zhang , Tianxiang Cui","doi":"10.1016/j.swevo.2025.102238","DOIUrl":"10.1016/j.swevo.2025.102238","url":null,"abstract":"<div><div>The United Multi-Operator Evolutionary Algorithms (UMOEAs) demonstrate high performance in evolutionary computation due to their combination of various operators for Differential Evolution (DE) variants and local search mechanisms, and have evolved into three versions over the last decade. In this work, a novel variant of the UMOEAs is proposed, named UMOEAs-IV. UMOEAs-IV employs a novel calculation method for the scaling factor <span><math><mi>F</mi></math></span>, which generates dynamic values to adaptively control the step size of mutation operators, a mutation strategy with complementary operators that better balance exploration and exploitation, an Estimation-of-Distribution Algorithm (EDA) to learn the probabilistic distribution of promising individuals, and a stagnation strategy to help individuals escape local optima. UMOEAs-IV is compared with 15 recently proposed DE-based algorithms and tested on the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark functions, showing superior performance over all of them. It is also applied to livestock feed ration optimization for beef and dairy cattle, where it shows top performance in the experimental results. The source code of UMOEAs-IV is provided at <span><span>https://github.com/microhard1999/CODES</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102238"},"PeriodicalIF":8.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684857","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}
Pub Date : 2025-12-02DOI: 10.1016/j.swevo.2025.102242
Qingshan Tan , Changhe Li , Miqing Li , Shengxiang Yang
Designing multi-objective benchmark test functions is an important topic in the field of evolutionary multi-objective optimization because it can help researchers identify the strengths and weaknesses of algorithms and contribute to improving their performance. However, existing methods for constructing multi-objective test problems exhibit certain specific limitations, such as the homogeneous structure of objectives, regular shapes of the Pareto optimal sets, and so on. To address these issues, this paper proposes an object-oriented construction method that abstracts components of a multi-objective optimization problem’s fitness landscape into classes. By designing attributes of these components, such as size, shape, position, and quantity, diverse test classes can be generated. Through a combination of these attributes, test cases with varying difficulty levels and distinct characteristics can be constructed. Based on this generator, several novel features are displayed. Moreover, five classic multi-objective evolutionary algorithms are tested on them. The results show different behaviors of these algorithms on the constructed test cases. At the same time, these generated features pose significant challenges to the tested algorithms.
{"title":"Generating continuous multi-objective benchmark problems by the object-oriented method","authors":"Qingshan Tan , Changhe Li , Miqing Li , Shengxiang Yang","doi":"10.1016/j.swevo.2025.102242","DOIUrl":"10.1016/j.swevo.2025.102242","url":null,"abstract":"<div><div>Designing multi-objective benchmark test functions is an important topic in the field of evolutionary multi-objective optimization because it can help researchers identify the strengths and weaknesses of algorithms and contribute to improving their performance. However, existing methods for constructing multi-objective test problems exhibit certain specific limitations, such as the homogeneous structure of objectives, regular shapes of the Pareto optimal sets, and so on. To address these issues, this paper proposes an object-oriented construction method that abstracts components of a multi-objective optimization problem’s fitness landscape into classes. By designing attributes of these components, such as size, shape, position, and quantity, diverse test classes can be generated. Through a combination of these attributes, test cases with varying difficulty levels and distinct characteristics can be constructed. Based on this generator, several novel features are displayed. Moreover, five classic multi-objective evolutionary algorithms are tested on them. The results show different behaviors of these algorithms on the constructed test cases. At the same time, these generated features pose significant challenges to the tested algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102242"},"PeriodicalIF":8.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684940","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}
Pub Date : 2025-12-01DOI: 10.1016/j.swevo.2025.102225
Xinxin Zhou , Xiangzhi Wu , Mingwei Wang , Youzhi Sun
The rise in sedentary lifestyles of people has led to a range of health problems, illustrating the need for innovative solutions to promote physical activity. Intelligent sports equipment has become important in promoting physical activity by providing real-time feedback and personalized guidance. However, the effectiveness of such equipment is often limited by suboptimal route planning and user interface design. Traditional algorithms frequently experience premature convergence and limited route diversity, leading to suboptimal navigation paths. Therefore, we proposed a novel Random Replacement based Slime Mold Optimization (RR-SMO) algorithm with a human-centered interface design for route optimization in outdoor sports equipment. The Random Replacement Strategy (RRS) is introduced into the algorithm to selectively adjust the dimensions of the best solution. This strategy helps to avoid premature convergence, boosts convergence speed, and ensures a diverse population, which contributes to more accurate and varied route planning in intricate navigation scenarios. Simultaneously, the interactive interface design for route navigation is designed with clear iconography, optimized touch targets, and adaptive visual cues to improve user experience. The comprehensive experimental assessment of the RR-SMO algorithm against existing algorithms using diverse metrics revealed that the RR-SMO algorithm achieves a navigation efficiency of 98.75 %, waypoint accuracy of 99.32 %, convergence time of 15 seconds, algorithm robustness of 97.82 %, a response time of 112 milliseconds, and an error rate of 5 %. The RR-SMO algorithm surpassed baseline methods by identifying optimal waypoints effectively and provides a scalable, intelligent solution for outdoor sports navigation to support healthy lifestyles.
{"title":"RR-SMO: A novel optimization algorithm for enhancing route efficiency in outdoor sports equipment","authors":"Xinxin Zhou , Xiangzhi Wu , Mingwei Wang , Youzhi Sun","doi":"10.1016/j.swevo.2025.102225","DOIUrl":"10.1016/j.swevo.2025.102225","url":null,"abstract":"<div><div>The rise in sedentary lifestyles of people has led to a range of health problems, illustrating the need for innovative solutions to promote physical activity. Intelligent sports equipment has become important in promoting physical activity by providing real-time feedback and personalized guidance. However, the effectiveness of such equipment is often limited by suboptimal route planning and user interface design. Traditional algorithms frequently experience premature convergence and limited route diversity, leading to suboptimal navigation paths. Therefore, we proposed a novel Random Replacement based Slime Mold Optimization (RR-SMO) algorithm with a human-centered interface design for route optimization in outdoor sports equipment. The Random Replacement Strategy (RRS) is introduced into the algorithm to selectively adjust the dimensions of the best solution. This strategy helps to avoid premature convergence, boosts convergence speed, and ensures a diverse population, which contributes to more accurate and varied route planning in intricate navigation scenarios. Simultaneously, the interactive interface design for route navigation is designed with clear iconography, optimized touch targets, and adaptive visual cues to improve user experience. The comprehensive experimental assessment of the RR-SMO algorithm against existing algorithms using diverse metrics revealed that the RR-SMO algorithm achieves a navigation efficiency of 98.75 %, waypoint accuracy of 99.32 %, convergence time of 15 seconds, algorithm robustness of 97.82 %, a response time of 112 milliseconds, and an error rate of 5 %. The RR-SMO algorithm surpassed baseline methods by identifying optimal waypoints effectively and provides a scalable, intelligent solution for outdoor sports navigation to support healthy lifestyles.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"100 ","pages":"Article 102225"},"PeriodicalIF":8.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684938","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}
Pub Date : 2025-12-01DOI: 10.1016/j.swevo.2025.102241
Shengchang Li , Peilan Xu , Zhenglong Ding , Ziqian Kong , Wenjian Luo
The berth allocation and crane assignment problems (BACAPs) present significant challenges in marine transportation. While various mathematical solvers and heuristic algorithms exist for addressing these issues, they often suffer from time-consuming planning processes in complex and uncertain environments. Despite the introduction of reinforcement learning (RL), these studies typically focus solely on discrete berth allocation problems, disregarding crane assignment considerations. To facilitate an RL-based solution, this paper formulates a mathematical model for the continuous BACAP to define the decision environment. The model incorporates consecutive vessel berth positions, time-variant quay crane (QC) assignment, the maximum number of QCs per vessel, the cross-movement of QCs, and the safe distance between vessels. Subsequently, we propose an evolutionary reinforcement learning (ERL) framework as a novel solution to complex BACAPs. We establish an event-triggered Markov decision process (ET-MDP) and devise a feasibility-aware policy network using gated recurrent units (GRUs) and attention mechanisms. Additionally, we propose an evolution strategy based on density-based behavioral diversity enhancement (ES-DDE) as an optimizer for the policy network. This mechanism employs a niching strategy to construct multiple sub-populations and establish density landscapes. It drives each sub-population to explore low-density areas to increase the agent’s behavioral diversity and mitigate bias induced by deceptive rewards. Finally, we evaluate the performance of the proposed ERL-DDE on a set of 15 BACAP cases. Comparative analysis against several state-of-the-art heuristics and RL algorithms demonstrates the superior performance of ERL-DDE.
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