With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.
{"title":"Multi-Objective Binary Whale Optimization-Based Virtual Machine Allocation in Cloud Environments","authors":"Ankit Srivastava, Narander Kumar","doi":"10.4018/ijsir.317111","DOIUrl":"https://doi.org/10.4018/ijsir.317111","url":null,"abstract":"With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48203367","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}
Economic load dispatch is to operate thermal generators economically with fulfilling load demand. This economic dispatch problem becomes highly complex and non-linear after considering various operating constraints like valve-point loading effect, generator operating constraints, and prohibited operating zone. The recently developed physics law-based artificial electric field algorithm has been applied to solve highly complex and non-linear ELD problems. The exploration and exploitation strategy of the algorithm helps to avoid local optimum value, and to get global optimum value in less computation time. The AEFA method has been applied to 10, 13, 15, 40, and large 110 thermal generators to validate the effectiveness of the proposed algorithm. The results obtained by the proposed algorithm have been compared with other recently developed algorithms.
{"title":"Artificial Electric Field Algorithm Applied to the Economic Load Dispatch Problem With Valve Point Loading Effect","authors":"Kathan Shah, Jatin M. Soni, K. Bhattacharjee","doi":"10.4018/ijsir.317136","DOIUrl":"https://doi.org/10.4018/ijsir.317136","url":null,"abstract":"Economic load dispatch is to operate thermal generators economically with fulfilling load demand. This economic dispatch problem becomes highly complex and non-linear after considering various operating constraints like valve-point loading effect, generator operating constraints, and prohibited operating zone. The recently developed physics law-based artificial electric field algorithm has been applied to solve highly complex and non-linear ELD problems. The exploration and exploitation strategy of the algorithm helps to avoid local optimum value, and to get global optimum value in less computation time. The AEFA method has been applied to 10, 13, 15, 40, and large 110 thermal generators to validate the effectiveness of the proposed algorithm. The results obtained by the proposed algorithm have been compared with other recently developed algorithms.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47931623","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}
In this paper, the real-time changes of demand-side response factors are accurately considered. First, CNN is combined with BiLSTM network to extract the spatio-temporal features of load data; then an attention mechanism is introduced to automatically assign the corresponding weights to the hidden layer states of BiLSTM. In the optimization part of the network parameters, the PSO algorithm is combined to obtain better model parameters. Then, considering the average reduction rate of various users under energy efficiency resources and the average load rate under load resources on the original forecast load and the original forecast load, the original load is superimposed with the response load considering demand-side resources to achieve accurate load forecast. Finally, “price-based” time-of-use tariff and “incentive-based” emergency demand response are selected to build a load response model based on the principle of maximizing customer benefits. The results show that demand-side response can reduce the frequency and magnitude of price fluctuations in the wholesale market.
{"title":"Research on Electric Load Forecasting and User Benefit Maximization Under Demand-Side Response","authors":"Wenna Zhao, Guoxing Mu, Yanfang Zhu, Limei Xu, Deliang Zhang, Hongwei Huang","doi":"10.4018/ijsir.317112","DOIUrl":"https://doi.org/10.4018/ijsir.317112","url":null,"abstract":"In this paper, the real-time changes of demand-side response factors are accurately considered. First, CNN is combined with BiLSTM network to extract the spatio-temporal features of load data; then an attention mechanism is introduced to automatically assign the corresponding weights to the hidden layer states of BiLSTM. In the optimization part of the network parameters, the PSO algorithm is combined to obtain better model parameters. Then, considering the average reduction rate of various users under energy efficiency resources and the average load rate under load resources on the original forecast load and the original forecast load, the original load is superimposed with the response load considering demand-side resources to achieve accurate load forecast. Finally, “price-based” time-of-use tariff and “incentive-based” emergency demand response are selected to build a load response model based on the principle of maximizing customer benefits. The results show that demand-side response can reduce the frequency and magnitude of price fluctuations in the wholesale market.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49423852","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}
Dynamic economic dispatch (DED) deals with the allocation of predicted load demand over a certain period of time among the thermal generating units at minimum fuel cost. The objective function of DED becomes highly complex and nonlinear after considering various operating constraints like valve point loading, ramp rate limit, transmission loss, and generation limits. In this study, the sine-cosine algorithm has been presented to solve the DED problem with various constraints. The randomly placed swarm finds an optimum solution according to their fitness values and keeps the path towards the best solution attained by each swarm. The swarm avoid local optima in the exploration stage and move towards the solution exploitation stage using sine and cosine functions. The proposed technique has been tested in several test systems. The results obtained by the proposed technique have been compared with those obtained by other published methods employing the same test systems. The results validate the superiority and the effectiveness of the proposed technique over other well-known techniques.
{"title":"Sine-Cosine Algorithm for the Dynamic Economic Dispatch Problem With the Valve-Point Loading Effect","authors":"Jatin M. Soni, K. Bhattacharjee","doi":"10.4018/ijsir.316801","DOIUrl":"https://doi.org/10.4018/ijsir.316801","url":null,"abstract":"Dynamic economic dispatch (DED) deals with the allocation of predicted load demand over a certain period of time among the thermal generating units at minimum fuel cost. The objective function of DED becomes highly complex and nonlinear after considering various operating constraints like valve point loading, ramp rate limit, transmission loss, and generation limits. In this study, the sine-cosine algorithm has been presented to solve the DED problem with various constraints. The randomly placed swarm finds an optimum solution according to their fitness values and keeps the path towards the best solution attained by each swarm. The swarm avoid local optima in the exploration stage and move towards the solution exploitation stage using sine and cosine functions. The proposed technique has been tested in several test systems. The results obtained by the proposed technique have been compared with those obtained by other published methods employing the same test systems. The results validate the superiority and the effectiveness of the proposed technique over other well-known techniques.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46559037","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}
Fazli Wahid, R. Ghazali, L. H. Ismail, Ali M. Algarwi Aseere
In this work, hourly and daily energy consumption prediction has been carried out using multi-layer feed forward neural network. The network designed in the proposed architecture has three layers, namely input layer, hidden layer, and output layer. The input layer had eight neurons, output layer had one neuron, and the number of neurons in the hidden layer was varied to find an optimal number for accurate prediction. Different parameters of the neural network were varied repeatedly, and the prediction accuracy was observed for each combination of different parameters to find an optimized combination of different parameters. For hourly energy consumption prediction, a total of six weeks data (September 1 to October 12, 2004) of 10 residential buildings has been used whereas for daily energy consumption prediction, a total of 52 weeks data (January 2004 to December 2004) of 30 residential buildings has been used. To evaluate the performance of the proposed approach, different performance evaluation measurements were applied.
{"title":"An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings","authors":"Fazli Wahid, R. Ghazali, L. H. Ismail, Ali M. Algarwi Aseere","doi":"10.4018/ijsir.316649","DOIUrl":"https://doi.org/10.4018/ijsir.316649","url":null,"abstract":"In this work, hourly and daily energy consumption prediction has been carried out using multi-layer feed forward neural network. The network designed in the proposed architecture has three layers, namely input layer, hidden layer, and output layer. The input layer had eight neurons, output layer had one neuron, and the number of neurons in the hidden layer was varied to find an optimal number for accurate prediction. Different parameters of the neural network were varied repeatedly, and the prediction accuracy was observed for each combination of different parameters to find an optimized combination of different parameters. For hourly energy consumption prediction, a total of six weeks data (September 1 to October 12, 2004) of 10 residential buildings has been used whereas for daily energy consumption prediction, a total of 52 weeks data (January 2004 to December 2004) of 30 residential buildings has been used. To evaluate the performance of the proposed approach, different performance evaluation measurements were applied.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49196228","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 : 2023-01-01DOI: 10.1504/ijsi.2023.10055513
Sunita Chahar, D. K. Yadav
{"title":"Retrospection and investigation of ANN-based MPPT technique in comparison with soft computing-based MPPT techniques for PV solar and wind energy generation system","authors":"Sunita Chahar, D. K. Yadav","doi":"10.1504/ijsi.2023.10055513","DOIUrl":"https://doi.org/10.1504/ijsi.2023.10055513","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"216 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75594162","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}
Video summarization is used to generate a short summary video for providing the users a very useful visual and synthetic abstract of the video content. There are various methods are developed for video summarization in existing, still an effective method is required due to some drawbacks, like cost and time. The ultimate goal of the research is to concentrate on an effective video summarization methodology that represents the development of short summary from the entire video stream in an effective manner. At first, the input cricket video consisting of number of frames is given to the keyframe generation phase, which is performed based on Discrete Cosine Transform (DCT) and Euclidean distance for obtaining the keyframes. Then, the residual keyframe generation is carried out based on Deep Convolutional Neural Network (DCNN), which is trained optimally using the proposed Exponential weighed moving average-Jaya (EWMA-Jaya) optimization.
{"title":"Local Optimal-Oriented Pattern and Exponential Weighed-Jaya Optimization-Based Deep Convolutional Networks for Video Summarization","authors":"L. Jimson., John Patrick Ananth","doi":"10.4018/ijsir.304403","DOIUrl":"https://doi.org/10.4018/ijsir.304403","url":null,"abstract":"Video summarization is used to generate a short summary video for providing the users a very useful visual and synthetic abstract of the video content. There are various methods are developed for video summarization in existing, still an effective method is required due to some drawbacks, like cost and time. The ultimate goal of the research is to concentrate on an effective video summarization methodology that represents the development of short summary from the entire video stream in an effective manner. At first, the input cricket video consisting of number of frames is given to the keyframe generation phase, which is performed based on Discrete Cosine Transform (DCT) and Euclidean distance for obtaining the keyframes. Then, the residual keyframe generation is carried out based on Deep Convolutional Neural Network (DCNN), which is trained optimally using the proposed Exponential weighed moving average-Jaya (EWMA-Jaya) optimization.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"13 1","pages":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470796","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 : 2022-01-01DOI: 10.1504/ijsi.2022.10051132
Harish Sharma, Nirmala Sharma, R. Chauhan
{"title":"A review of 0-1 knapsack problem by nature-inspired optimisation algorithms","authors":"Harish Sharma, Nirmala Sharma, R. Chauhan","doi":"10.1504/ijsi.2022.10051132","DOIUrl":"https://doi.org/10.1504/ijsi.2022.10051132","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"48 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74701223","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 : 2022-01-01DOI: 10.1504/ijsi.2022.10044826
A. Jain, Surabhi N.A.
{"title":"Twitter sentiment analysis on Indian Government schemes using machine learning models","authors":"A. Jain, Surabhi N.A.","doi":"10.1504/ijsi.2022.10044826","DOIUrl":"https://doi.org/10.1504/ijsi.2022.10044826","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"20 7 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83878282","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 : 2022-01-01DOI: 10.4018/ijsir.2022010112
This paper presents an application of Water Cycle algorithm (WCA) in solving stochastic programming problems. In particular, Linear stochastic fractional programming problems are considered which are solved by WCA and solutions are compared with Particle Swarm Optimization, Differential Evolution, and Whale Optimization Algorithm and the results from literature. The constraints are handled by converting constrained optimization problem into an unconstrained optimization problem using Augmented Lagrangian Method. Further, a fractional stochastic transportation problem is examined as an application of the stochastic fractional programming problem. In terms of efficiency of algorithms and the ability to find optimal solutions, WCA gives highly significant results in comparison with the other metaheuristic algorithms and the quoted results in the literature which demonstrates that WCA algorithm has 100% convergence in all the problems. Moreover, non-parametric hypothesis tests are performed and which indicates that WCA presents better results as compare to the other algorithms.
{"title":"Application of Water Cycle algorithm to Stochastic Fractional Programming Problem","authors":"","doi":"10.4018/ijsir.2022010112","DOIUrl":"https://doi.org/10.4018/ijsir.2022010112","url":null,"abstract":"This paper presents an application of Water Cycle algorithm (WCA) in solving stochastic programming problems. In particular, Linear stochastic fractional programming problems are considered which are solved by WCA and solutions are compared with Particle Swarm Optimization, Differential Evolution, and Whale Optimization Algorithm and the results from literature. The constraints are handled by converting constrained optimization problem into an unconstrained optimization problem using Augmented Lagrangian Method. Further, a fractional stochastic transportation problem is examined as an application of the stochastic fractional programming problem. In terms of efficiency of algorithms and the ability to find optimal solutions, WCA gives highly significant results in comparison with the other metaheuristic algorithms and the quoted results in the literature which demonstrates that WCA algorithm has 100% convergence in all the problems. Moreover, non-parametric hypothesis tests are performed and which indicates that WCA presents better results as compare to the other algorithms.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41744709","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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