A Heuristic Framework to Organize Heterogenous Network Embedded Systems

This paper introduces a heuristic method to process sequencing in Heterogeneous Distributed Embedded Systems (HDES). This suggested study discusses the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) enhanced using the greedy algorithm added to the system, which consists of numerous Heterogeneous Embedded Machines (HEMs) operating as a group. Customers have the ability to make use of their computing capacity remotely. The connectivity options on various kinds of buses are considered to identify the best option. Novel meta-heuristic data based on transporting reliant is proposed to increase the chance of ACO generating task prioritization. Furthermore, a greedy approach for machine assignment is used to carry out task planning. Simulations using arbitrary task graphs in the HEM collection show that the Enhanced Greedy Ant Colony Optimization (EGACO) process exceeds each of the others by 33% in terms of outcome eminence.