Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.10059921
Min Xu, Xiaoyu Li, Pengfei Jia, Lin Zhang
{"title":"Classification Techniques of Electronic Nose: A Review","authors":"Min Xu, Xiaoyu Li, Pengfei Jia, Lin Zhang","doi":"10.1504/ijbic.2023.10059921","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10059921","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Slime mould algorithm (SMA) is a new meta-heuristic algorithm which imitates the biological mechanism of natural creatures. It has good initial performance, but it also has some disadvantages. More importantly, the bionic modelling of SMA is not complete, and many biological mechanisms of slime moulds are ignored. This paper proposes an improved slime mould algorithm by perfecting bionic mechanism (IBSMA). Specifically, three mechanisms are added. Among them, the 'polar growth' mechanism is used to improve the global optimisation ability, the 'memory' mechanism is used to enhance the ability of the algorithm to jump out of the local optimum, and the 'amoeba' mechanism is used to expand the search space and improve the exploration capability of the algorithm. Qualitative and effectiveness analyses are conducted, and the proposed algorithm is compared with nine excellent algorithms. The results show that IBSMA has the best performance, which is also verified by non-parametric statistical methods.
{"title":"Improved slime mould algorithm by perfecting bionic-based mechanisms","authors":"Tianyu Yu, Jiawen Pan, N.A. Qian, Miao Song, Jibin Yin, Yong Feng, Yunfa Fu, Yingna Li","doi":"10.1504/ijbic.2023.133504","DOIUrl":"https://doi.org/10.1504/ijbic.2023.133504","url":null,"abstract":"Slime mould algorithm (SMA) is a new meta-heuristic algorithm which imitates the biological mechanism of natural creatures. It has good initial performance, but it also has some disadvantages. More importantly, the bionic modelling of SMA is not complete, and many biological mechanisms of slime moulds are ignored. This paper proposes an improved slime mould algorithm by perfecting bionic mechanism (IBSMA). Specifically, three mechanisms are added. Among them, the 'polar growth' mechanism is used to improve the global optimisation ability, the 'memory' mechanism is used to enhance the ability of the algorithm to jump out of the local optimum, and the 'amoeba' mechanism is used to expand the search space and improve the exploration capability of the algorithm. Qualitative and effectiveness analyses are conducted, and the proposed algorithm is compared with nine excellent algorithms. The results show that IBSMA has the best performance, which is also verified by non-parametric statistical methods.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135556020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-01-01DOI: 10.1504/ijbic.2023.132760
Lei Kou, Fangfang Zhang, Luobing Chen, Wende Ke, Quande Yuan, Junhe Wan, Zhen Wang
{"title":"An algorithm of finding rules for a class of cellular automata","authors":"Lei Kou, Fangfang Zhang, Luobing Chen, Wende Ke, Quande Yuan, Junhe Wan, Zhen Wang","doi":"10.1504/ijbic.2023.132760","DOIUrl":"https://doi.org/10.1504/ijbic.2023.132760","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136028384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-01-01DOI: 10.1504/ijbic.2023.10056898
Guangzhu Tan, Jiejie Tian, Min Gao, Shuai Zhang, Xu Wang, Biyu Yang, Linda Yang, Jiafu Su
{"title":"Joint modelling of task requirements and worker preferences based on heterogeneous features and multiple interactions for knowledge-intensive crowdsourcing recommendation","authors":"Guangzhu Tan, Jiejie Tian, Min Gao, Shuai Zhang, Xu Wang, Biyu Yang, Linda Yang, Jiafu Su","doi":"10.1504/ijbic.2023.10056898","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10056898","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"73 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90615835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-01-01DOI: 10.1504/ijbic.2023.10060417
Zhen Wang, Yan Bai, Dongxin Lu, Qingfeng Li, Lei Kou, Ke Wende, Yuhan Chen, Junhe Wan
{"title":"Multiple Channel Adjustment based on Composite Backbone Network for Underwater Image Enhancement","authors":"Zhen Wang, Yan Bai, Dongxin Lu, Qingfeng Li, Lei Kou, Ke Wende, Yuhan Chen, Junhe Wan","doi":"10.1504/ijbic.2023.10060417","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10060417","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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