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Adopting an Improved Genetic Algorithm for Multi-Objective Service Composition Optimization in Smart Agriculture

Authors

  • Shalini Sharma Jaypee University of Information Technology
  • Bhupendra Kumar Pathak Assistant Professor
  • Rajiv Kumar

Abstract

In order to modernize numerous areas, the Internet of Things (IoT) is an emerging paradigm that connects various intelligent physical objects. As the rising global population depletes resources and causes unforeseeable environmental changes, producing sufficient food has now become a prime concern globally. Hence, to resolve this issue, agriculture is shifting to "smart agriculture," whose focus is to accelerate production using wireless sensor networks, cloud computing and IoT. The service composition is thought to be a crucial component in this technology for increasing functionalities and satisfying user's complex needs. This paper presents an improved version of the multi-objective genetic algorithm (iMOGA) for optimizing the time and cost associated with the services involved in the production of apple orchards to maximize the farmer's financial goals while reducing their potential time. It has been observed that (iMOGA) is a promising approach to obtaining Pareto optimal solutions for service composition optimization in smart agriculture.

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How to Cite

Sharma, S., Pathak, B. K., & Kumar, R. Adopting an Improved Genetic Algorithm for Multi-Objective Service Composition Optimization in Smart Agriculture. Austrian Journal of Statistics, 53(5), 11–25. Retrieved from https://ajs.or.at/index.php/ajs/article/view/1874

Issue

Vol. 53 No. 5 (2024): Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence

Section

Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence

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