336 research outputs found
New formulations for mechanical properties of recycled aggregate concrete using gene expression programming
No full textAbstract not availableAliakbar Gholampour, Amir H. Gandomi, Togay Ozbakkalogl
Multi-population Evolutionary and Swarm Intelligence Dynamic Optimization Algorithms: A Survey
No full textMulti-population evolutionary and swarm intelligence dynamic optimization algorithms are the most flexible and effective methods for solving dynamic optimization problems. In a dynamic optimization problem, the search space is affected by environmental changes over time. In multi-population evolutionary and swarm intelligence dynamic optimization algorithms, the number of subpopulations is a parameter determined either by the user or adaptively. The use of multiple sub-populations enables these methods to efficiently track the moving optimum. These methods are capable of gathering historical knowledge about the search space, which is used to effectively react to changes and provide a warmed-up start for the algorithm in new environments. In this chapter, the components of multi-population algorithms are classified to the ones that are used for subpopulation formation, management of computational resources, transmission of information from previous environments, and handling diversity loss. Based on this classification, researchers can have a better understanding of how these components make evolutionary and swarm intelligence algorithms capable of addressing the challenges of dynamic optimization problems
An introduction of Krill Herd algorithm for engineering optimization
No full textA new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.
First published online: 25 Aug 201
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since March 24th, 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since March 24th, 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
[Dataset is updated Twice a Week]
The Authors can Refer to and CITE our latest Papers on COVID:
1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118
2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.10994
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118
2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.109945
.
[Dataset is updated Once a Week
COVID-19: Time Series Datasets India versus World
No full textThis dataset consists of COVID-19 time series data of India since 24th March 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : [email protected]) for more details.
.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Evolutionary Modelling of the COVID-19 Pandemic in Fifteen Most Affected Countries" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.110118
2. Rohit Salgotra, Mostafa Gandomi, Amir H Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" Chaos, Solitons \& Fractals: (2020). https://doi.org/10.1016/j.chaos.2020.109945
.
[Dataset is updated Once a Week
- …
