{"title":"GPGPU based Dual Population Genetic Algorithm for solving Constrained Optimization Problem","authors":"A. Umbarkar, P. D. Sheth","doi":"10.37394/232027.2022.4.3","DOIUrl":"https://doi.org/10.37394/232027.2022.4.3","url":null,"abstract":"Dual Population Genetic Algorithm is a variant of Genetic Algorithm that provides additional diversity to the main population. It covers the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. But also its additional population introduces large search space that increases time required to find an optimal solution. This large scale search space problem can be easily solved using consumer-level graphics cards. The solution obtained using accelerated DPGA for solving a constrained optimization problem from CEC 2006 is compared with the obtained solution using sequential algorithm. The results show speed up maintaining solution quality.","PeriodicalId":145183,"journal":{"name":"International Journal of Electrical Engineering and Computer Science","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637605","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}
{"title":"Polynomially Solvable and NP-hard Special Cases for Scheduling with Heads and Tails","authors":"Elisa Chinos, N. Vakhania","doi":"10.37394/232027.2022.4.2","DOIUrl":"https://doi.org/10.37394/232027.2022.4.2","url":null,"abstract":"We consider a basic single-machine scheduling problem when jobs have release and delivery times and the objective is to minimize maximum job lateness. This problem is known to be strongly NP-hard. We study inherent structure of this problem exploring its special cases and the conditions when the problem can be efficiently solved.","PeriodicalId":145183,"journal":{"name":"International Journal of Electrical Engineering and Computer Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129329158","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}
{"title":"A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction","authors":"E. Gjika, L. Basha","doi":"10.37394/232027.2022.4.1","DOIUrl":"https://doi.org/10.37394/232027.2022.4.1","url":null,"abstract":"The objective of this study is to analyze and compare classical time series and deep learning models for energy load prediction. Energy predictions are important for management and sustainable systems. After analyzing the climacteric factors impact on energy load (a case study in Albania) we considered classical and deep learning models to perform forecasts. We have used hourly and daily time series for a period of three years. In total respectively 26,280 hours and 1095 days. Average temperature is considered as external variable in both statistical and deep learning models. The dynamic evolution of hourly (daily) load is correlated with hourly (daily) average temperature. The performance of the proposed models is analyzed and evaluated based on accuracy measurements (MSE, RMSE, MAPE, AIC, BIC etc.) and graphics results of statistical tests. In-sample and out-of-sample accuracy is evaluated. The models show competitive performance to some recent works in the field of short-and medium-term energy load forecasts. This work may be used by stakeholders to optimize their activities and obtain accurate forecasts of energy system behavior.","PeriodicalId":145183,"journal":{"name":"International Journal of Electrical Engineering and Computer Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775969","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}