Pub Date : 2023-04-29DOI: 10.1007/s12065-023-00850-2
W. Zhong, Li Mao, Yang Ning
{"title":"Fingerprint image denoising and inpainting using generative adversarial networks","authors":"W. Zhong, Li Mao, Yang Ning","doi":"10.1007/s12065-023-00850-2","DOIUrl":"https://doi.org/10.1007/s12065-023-00850-2","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43281765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-21DOI: 10.1007/s12065-023-00846-y
Lydia Sonia Bendimerad, Habiba Drias
The Artificial Orca Algorithm (AOA) is an existing swarm intelligence algorithm, empowered in this paper by two well-known mutation operators and opposition-based learning, yielding the novel methods Deep Self-Learning Artificial Orca Algorithm (DSLAOA), Opposition Deep Self-Learning Artificial Orca Algorithm (ODSLAOA), and Opposition Artificial Orca Learning Algorithm. The DSLAOA and ODSLAOA are based on the Cauchy and Gauss mutation operators. Their effectiveness is evaluated on both continuous and discrete problems. The suggested algorithms are tested and compared to seven recent state-of-the-art metaheuristics in the continuous context. The results demonstrate that, when compared to the other algorithms, DSLAOA based on the Cauchy operator is the most effective technique. After that, a specific real-world scenario involving emergency medical services in a dire situation is tackled. The Ambulance Dispatching and Emergency Calls Covering Problem is the addressed problem, and a mathematical formulation is made to model this issue. AOA, DSLAOAC, and DSLAOAG are tested and contrasted with a successful recent heuristic in this field. The experiments are run on real data, and the results show that the swarm approaches are effective and helpful in determining the resources required in this kind of emergency.
{"title":"Intelligent contributions of the artificial orca algorithm for continuous problems and real-time emergency medical services.","authors":"Lydia Sonia Bendimerad, Habiba Drias","doi":"10.1007/s12065-023-00846-y","DOIUrl":"10.1007/s12065-023-00846-y","url":null,"abstract":"<p><p>The Artificial Orca Algorithm (AOA) is an existing swarm intelligence algorithm, empowered in this paper by two well-known mutation operators and opposition-based learning, yielding the novel methods Deep Self-Learning Artificial Orca Algorithm (DSLAOA), Opposition Deep Self-Learning Artificial Orca Algorithm (ODSLAOA), and Opposition Artificial Orca Learning Algorithm. The DSLAOA and ODSLAOA are based on the Cauchy and Gauss mutation operators. Their effectiveness is evaluated on both continuous and discrete problems. The suggested algorithms are tested and compared to seven recent state-of-the-art metaheuristics in the continuous context. The results demonstrate that, when compared to the other algorithms, DSLAOA based on the Cauchy operator is the most effective technique. After that, a specific real-world scenario involving emergency medical services in a dire situation is tackled. The Ambulance Dispatching and Emergency Calls Covering Problem is the addressed problem, and a mathematical formulation is made to model this issue. AOA, DSLAOAC, and DSLAOAG are tested and contrasted with a successful recent heuristic in this field. The experiments are run on real data, and the results show that the swarm approaches are effective and helpful in determining the resources required in this kind of emergency.</p>","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":"1-36"},"PeriodicalIF":2.6,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9706580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-19DOI: 10.1007/s12065-023-00848-w
Tuan-Anh Do, H. Ban, Thi Thanh Binh Huynh, Minh Tu Le, Binh Long Nguyen
{"title":"Genetic algorithm based approach to solve the Clustered Steiner Tree Problem","authors":"Tuan-Anh Do, H. Ban, Thi Thanh Binh Huynh, Minh Tu Le, Binh Long Nguyen","doi":"10.1007/s12065-023-00848-w","DOIUrl":"https://doi.org/10.1007/s12065-023-00848-w","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48424684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.1007/s12065-023-00847-x
Ali Kooshari, Mehdi Fartash, Parastoo Mihannezhad, Meysam Chahardoli, Javad AkbariTorkestani, S. Nazari
{"title":"An optimization method in wireless sensor network routing and IoT with water strider algorithm and ant colony optimization algorithm","authors":"Ali Kooshari, Mehdi Fartash, Parastoo Mihannezhad, Meysam Chahardoli, Javad AkbariTorkestani, S. Nazari","doi":"10.1007/s12065-023-00847-x","DOIUrl":"https://doi.org/10.1007/s12065-023-00847-x","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41693467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1007/s12065-023-00839-x
Xuefen Chen, Chunming Ye, Yang Zhang, Lingwei Zhao, Jing Guo, Kun Ma
The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1–f7) and six multimodal tasks (f8–f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).
{"title":"Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks","authors":"Xuefen Chen, Chunming Ye, Yang Zhang, Lingwei Zhao, Jing Guo, Kun Ma","doi":"10.1007/s12065-023-00839-x","DOIUrl":"https://doi.org/10.1007/s12065-023-00839-x","url":null,"abstract":"The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1–f7) and six multimodal tasks (f8–f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135543664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1007/s12065-023-00840-4
Zahra Alijani, B. Shiri, I. Perfilieva, D. Baleanu
{"title":"Numerical solution of a new mathematical model for intravenous drug administration","authors":"Zahra Alijani, B. Shiri, I. Perfilieva, D. Baleanu","doi":"10.1007/s12065-023-00840-4","DOIUrl":"https://doi.org/10.1007/s12065-023-00840-4","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47476544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-28DOI: 10.1007/s12065-023-00844-0
Vanessa Ramírez-Pérez, José A. Guerrero-Díaz-de-León, J. Macías-Díaz
{"title":"On the detection of activity patterns in electromyographic signals via decision trees","authors":"Vanessa Ramírez-Pérez, José A. Guerrero-Díaz-de-León, J. Macías-Díaz","doi":"10.1007/s12065-023-00844-0","DOIUrl":"https://doi.org/10.1007/s12065-023-00844-0","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41587976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-21DOI: 10.1007/s12065-023-00845-z
Peipei Wang, Z. Zhao
{"title":"Improving context and syntactic dependency for aspect-based sentiment analysis using a fused graph attention network","authors":"Peipei Wang, Z. Zhao","doi":"10.1007/s12065-023-00845-z","DOIUrl":"https://doi.org/10.1007/s12065-023-00845-z","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44760138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-20DOI: 10.1007/s12065-023-00842-2
Caihong Zhang, Shengxiao Niu
{"title":"Adaptive industrial control data analysis based on deep learning","authors":"Caihong Zhang, Shengxiao Niu","doi":"10.1007/s12065-023-00842-2","DOIUrl":"https://doi.org/10.1007/s12065-023-00842-2","url":null,"abstract":"","PeriodicalId":46237,"journal":{"name":"Evolutionary Intelligence","volume":"16 1","pages":"1707 - 1715"},"PeriodicalIF":2.6,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41785240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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