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Task offloading in edge computing using integrated particle swarm optimization and genetic algorithm

Palaniappan Shabariram C., Ponnuswamy Priya P.

Advances in Science and Technology. Research Journal

2025

Vol. 19, no. 1

371--380

n the ever-evolving landscape of smart city applications and Intelligent Transport Systems, Vehicular Edge Computing emerged as a game-changing technology. Imagine a world where computational resources are no longer restricted to distant cloud servers but are brought nearer to the vehicles and users. Task offloading enables the computation in edge and cloud server. This proximity not only minimizes network latency but also enables a unfold of vehicles to process tasks at the edge, offering a swift and interactive response to the scenarios of applications with delay sensitivity. To deal with this constraint, an integrated methodology is utilized to enhance the offloading process. The proposed system integrates the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The integrated system optimizes task allocation by exploring the solution space effectively and ensuring efficient resource utilization while minimizing latency. In the evaluation, PSO+GA exhibits enhanced adaptability to varying task sizes, facilitating efficient offloading to the edge as needed. Energy efficiency varies between the algorithms, with PSO+GA generally showing minimal energy consumption. When compared to already existing algorithms such as Energy aware offloading, no offloading and random offloading, PSO+GA outperformed these algorithms in system performance and less energy consumption by a factor of 1.18.

A nature inspired hybrid partitional clustering method based on grey wolf optimization and JAYA algorithm

Shial Gyanaranjan, Saho Sabita, Panigrahi Sibarama

Computer Science

2023

T. 24 (3)

361--405

This paper presents a hybrid meta-heuristic algorithm that uses the grey wolf optimization (GWO) and the JAYA algorithm for data clustering. The idea is to use the explorative capability of the JAYA algorithm in the exploitative phase of GWO to form compact clusters. Here, instead of using only one best and one worst solution for generating offspring, the three best wolves (alpha, beta and delta) and three worst wolves of the population are used. So, the best and worst wolves assist in moving towards the most feasible solutions and simultaneously it helps to avoid from worst solutions; this enhances the chances of trapping at local optimal solutions. The superiority of the proposed algorithm is compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmark mathematical problems using the Friedman and Nemenyi hypothesis tests. Additionally, the superiority and robustness of our proposed algorithm is tested for 15 data clustering problems by using both Duncan's multiple range test and the Nemenyi hypothesis test.