Design of an Efficient Genetic Algorithm Model for Electric Load Balancing over Distributed Environments
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 6 |
| Year of Publication : 2023 |
| Authors : Abhay Kasetwar, Sagar Pradhan, Trupti Nagrare, Rahul Pethe, Ashwini Meshram |
10.14445/23488379/IJEEE-V10I6P112
How to Cite?
Abhay Kasetwar, Sagar Pradhan, Trupti Nagrare, Rahul Pethe, Ashwini Meshram, "Design of an Efficient Genetic Algorithm Model for Electric Load Balancing over Distributed Environments," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 6, pp. 114-119, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I6P112
Abstract:
To solve the issue of electric load balancing demand in distributed contexts, a genetic algorithm (GA) model is suggested in this study. The suggested model searches the problem sets for the best solutions using the genetic operators of crossover, mutation, and selection. This novel model aims to balance the electric load across multiple nodes in a distributed environment, thereby minimizing the overall energy consumption and ensuring that no node is overloaded. The suggested model considers each node's real-time power consumption and processing capabilities to establish the appropriate load distribution. To evaluate the efficiency of the proposed model, experiments were conducted on a simulated distributed environment with 20 nodes. The results showed that the proposed GA model achieved a load balance of up to 98%, with a reduction in energy consumption of up to 30%, compared to other existing load balancing techniques. The paper concludes that the proposed GA model is an efficient and effective solution to the problem of electric load balancing in distributed environments. The results show that the model may significantly decrease energy consumption while improving overall system performance. The suggested approach is applicable in various scenarios, including cloud computing, data centres, and smart grids, where efficient load balancing is critical for optimal system performance.
Keywords:
Distributed environment, Energy consumption, Genetic Algorithm (GA), Load balancing, Power efficiency.
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