There exists several complications with contemporary modes of transport in terms of security, accessibility, sustainability, and limitations in space. Vehicular networks are frequently seen as a viable strategy for enhancing ease and happiness in commuting. However, guaranteeing the optimal use of resources and addressing the rising needs continue to be difficult given the soaring popularity of vehicle users and the expanding diversity of requests for different services. By improving resource allocation from the perspectives of both suppliers and clients, the gaps in the prior research is closed and concentrate on resource allocation in vehicular cloud computing (VCC). The goal of our multi-objective optimisation approach for this issue is to increase levels of acceptance while conserving service supplier expenses. By adjusting the initial sample size in accordance with the pairing variable, unique overlap likelihood to favour exceptional members and increase demographic variation, we develop the Enhanced Ant lion optimization to handle such issues. The simulation results demonstrate that our suggested strategy outperforms the earlier methods in terms of performance.


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    Title :

    Enhanced Antlion optimization based resource allocation in vehicular cloud environment


    Contributors:
    Jayasudha, V. (author) / Nazrin Salma, S (author) / Saravanan, V. (author) / Nivedha, M. (author) / Vinuja, G. (author)


    Publication date :

    2023-11-24


    Size :

    269281 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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