Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.130040
Tingting Dong, Li Zhou, Lei Chen, Yanxing Song, Hengliang Tang, Huilin Qin
The advances in cloud computing promote the problem in processing speed. Computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. Efficient workflow scheduling is a challenge in reducing the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimisation problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.
{"title":"A hybrid algorithm for workflow scheduling in cloud environment","authors":"Tingting Dong, Li Zhou, Lei Chen, Yanxing Song, Hengliang Tang, Huilin Qin","doi":"10.1504/ijbic.2023.130040","DOIUrl":"https://doi.org/10.1504/ijbic.2023.130040","url":null,"abstract":"The advances in cloud computing promote the problem in processing speed. Computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. Efficient workflow scheduling is a challenge in reducing the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimisation problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"37 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":"136008733","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.2023.10057232
R. Mohanty, D. Chatterjee, S. Suman
{"title":"Power quality improvement for microgrid-connected PV-based converters under partial shading conditions using mixed optimization algorithms","authors":"R. Mohanty, D. Chatterjee, S. Suman","doi":"10.1504/ijbic.2023.10057232","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10057232","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"9 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88128246","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.2023.133508
Shelly Shiju George, R. Suji Pramila
{"title":"Improved whale social optimisation algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing","authors":"Shelly Shiju George, R. Suji Pramila","doi":"10.1504/ijbic.2023.133508","DOIUrl":"https://doi.org/10.1504/ijbic.2023.133508","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"82 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":"135556362","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.2023.10059186
Selvakumar R, NallappaBhavithran G
{"title":"Kernel Code for DNA Digital Data Storage","authors":"Selvakumar R, NallappaBhavithran G","doi":"10.1504/ijbic.2023.10059186","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10059186","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"28 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":"135496540","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.2023.133500
Renbin Xiao, Zhenhui Feng, Bowen Wu
This paper explores the emergence mechanism of collective intelligence (CI) from the complexity perspective. It begins with a comparison of the main features based on the two basic stages of CI, i.e., CI 1.0 (swarm intelligence) and CI 2.0 (crowd intelligence). Considering the connection mechanism between the two stages is still unclear, we would regard higher organism group behaviours as the transition between lower organism group behaviours to crowd behaviours. Accordingly, the bionic prototypes of CI can be classified into three categories: lower organisms, higher organisms and humans. This paper first refined the emergence mechanisms of CI in lower organisms represented by labour division, i.e., stimulus-response mechanism and activation-inhibition mechanism. Subsequently, the higher organism emergence mechanism was revealed, which is the attraction-repulsion mechanism based on roles division and perception driven. Furthermore, the emergence mechanism of crowd intelligence at the perceptual level and cognitive level are presented respectively, by means of process evolutionary description based on the attraction-repulsion mechanism. Finally, the research gives a holistic illustration of the emergence mechanism of CI.
{"title":"Research on emergence mechanism of collective intelligence from the complexity perspective","authors":"Renbin Xiao, Zhenhui Feng, Bowen Wu","doi":"10.1504/ijbic.2023.133500","DOIUrl":"https://doi.org/10.1504/ijbic.2023.133500","url":null,"abstract":"This paper explores the emergence mechanism of collective intelligence (CI) from the complexity perspective. It begins with a comparison of the main features based on the two basic stages of CI, i.e., CI 1.0 (swarm intelligence) and CI 2.0 (crowd intelligence). Considering the connection mechanism between the two stages is still unclear, we would regard higher organism group behaviours as the transition between lower organism group behaviours to crowd behaviours. Accordingly, the bionic prototypes of CI can be classified into three categories: lower organisms, higher organisms and humans. This paper first refined the emergence mechanisms of CI in lower organisms represented by labour division, i.e., stimulus-response mechanism and activation-inhibition mechanism. Subsequently, the higher organism emergence mechanism was revealed, which is the attraction-repulsion mechanism based on roles division and perception driven. Furthermore, the emergence mechanism of crowd intelligence at the perceptual level and cognitive level are presented respectively, by means of process evolutionary description based on the attraction-repulsion mechanism. Finally, the research gives a holistic illustration of the emergence mechanism of CI.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"3 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":"135555731","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.2023.10059274
Kameswara Rao M, Mohan Kumar Chandol
{"title":"Fr-ROA: Trust aware routing using fractional Remora Optimization Algorithm for secure communication in IoT","authors":"Kameswara Rao M, Mohan Kumar Chandol","doi":"10.1504/ijbic.2023.10059274","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10059274","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"274 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":"135600737","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 : 2022-01-01DOI: 10.1504/ijbic.2022.10044882
J. G. M. Soares, Pedro C. C. Pinto, J. Gomes, N. Nedjah, D. Vanzan, A. Pyrrho, André S. Pereira, Leonardo O. Mazza
{"title":"Deep convolutional neural network applied to Trypanosoma cruzi detection in blood samples","authors":"J. G. M. Soares, Pedro C. C. Pinto, J. Gomes, N. Nedjah, D. Vanzan, A. Pyrrho, André S. Pereira, Leonardo O. Mazza","doi":"10.1504/ijbic.2022.10044882","DOIUrl":"https://doi.org/10.1504/ijbic.2022.10044882","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"5 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87508373","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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