Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data

Opoku, Eugene A.; Ahmed, S. Ejaz; Song, Yin; Nathoo, Farouk S.

doi:10.3390/e23030329

Cited by 2 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main difference between the parts of the ACO algorithm is in the method of updating their pheromones [32]. The ACS algorithm is one of the most efficient ACO algorithms [33]. The main parameters using the algorithm are in Abbreviations.…”

Section: Ant Colony System (Acs)mentioning

confidence: 99%

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

et al. 2023

View full text Add to dashboard Cite

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO2 is reduced to negligible levels.

show abstract

Section: Ant Colony System (Acs)mentioning

confidence: 99%

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Pang et al [ 8 ] used a differential evolution algorithm and multitask learning to predict photovoltaic power. In the field of swarm intelligence algorithms, Opoku et al [ 9 ] combined an ant colony optimization algorithm with iterative conditional patterns for computing estimates of neural source activity. To optimize wireless sensor node deployment, Wu et al [ 10 ] proposed a virtual force-directed particle swarm optimization approach, where the optimization objective is to maximize network coverage.…”

Section: Introductionmentioning

confidence: 99%

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Yan

Liu

et al. 2023

Entropy

View full text Add to dashboard Cite

In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5–74% of the results is better than the ROA, and the robustness results of 66.67–75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

show abstract

Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data

Cited by 2 publications

References 41 publications

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Contact Info

Product

Resources

About