December 2025
Intermediate to advanced
656 pages
16h 47m
English
Content preview from Edge Artificial Intelligence
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3Role of Multi Objective Evolutionary Algorithms in Edge AI System Optimization
Preeti Gupta* and Sakshi Indolia
School of Technology Management & Engineering, SVKM’s NMIMS, Navi Mumbai, India
Abstract
Edge AI is a transformative technology that provides real-time results generated by complex artificial intelligence (AI) models deployed at the network edge. Running artificial intelligence models locally on edge devices limits performance due to different reasons such as computational resources, energy requirements, and latency issues. Due to the constrained nature of such environments, achieving successful deployment and operation requires balancing multiple, often conflicting, objectives. Optimizing multiple objectives (MO) ensures that the system delivers the desired performance while addressing constraints. Multi-Objective Evolutionary Algorithms (MOEAs) play a critical role in achieving Multiple Objective Optimization (MOO), especially in complex systems like Edge AI. The chapter delves into the integration of MOEAs with Edge AI technology for empowering the development of robust, flexible, and efficient Edge AI systems that meet diverse and conflicting goals.
Keywords: Edge AI, cloud AI, nature-inspired computing, multi-objective evolutionary algorithms
3.1 Introduction
Cloud AI process data received from a variety of sources and applies AI models on cloud servers, subsequently, the generated results are transmitted back to the edge devices. However, challenges such ...
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