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January 1, 2026International Journal of Engineering Systems Modelling and Simulation

Nature-inspired load balancing in cloud systems: a PSO-GA hybrid optimisation model

S. Balaji K. D. V. Prasad (18298120)S.Silvia Priscila P.M. Praveen

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Key Points

This research investigates a hybrid optimization model to improve load balancing in cloud computing using nature-inspired algorithms.
Developed a hybrid optimization model combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Implemented simulations to assess load balancing effectiveness in cloud systems.
Analyzed performance metrics including response time and resource utilization.
The PSO-GA hybrid model achieved a 30% increase in load balancing efficiency compared to traditional methods.
Response time decreased by an average of 20 ms (95% CI: 10-30 ms, p<0.01) after optimization.

Implication

Demonstrates enhanced load balancing performance in cloud systems, suggesting improved efficiency.

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Cite This Study

Balaji et al. (2026) studied this question.

synapsesocial.com/papers/6a2a528480c8f91e7f39e824 https://doi.org/10.1504/ijesms.2026.10079047

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1An efficient load balancing strategy using modified grey wolf optimiser for cloud computingOriginal Research · 2026
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4Robust optimisation-based load flow in uncertain DG-integrated unbalanced distribution systemsOriginal Research · 2026
5An Intelligent Study Towards Nature-Inspired Load Balancing Framework for Fog-Cloud Environments2024 · 1 citations

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