Analysis of Parity-Based Search Algorithms for Execution of Target Node in Relation to Automation Applications

In the era of Industry 4.0, machines are performing their assigned tasks smartly by using intelligent thinking approach. With the advancement in artificial intelligence-based algorithms, the machines are executing the decision nodes in relation to occurrence of situation from array of sensors for best judgement to actuate processes. In this regard, the processing unit of machine is trained to search for optimal solution in the data repository by using certain sequential and counter sequential approaches. The performance measures such as time and space complexity of these approaches directly pin pointing the placement of nodes and its web connection. Toward this challenge, in this paper, a new parity-based search algorithm is designed for search and execution of optimal node in relation to target node. The algorithm is genetically integrated with blind and heuristic search of node to explore different index wise parities. Sufficient number of iterative trails were done to measure the key performance parameters and comparative analysis were done for significant inferences. The algorithm is designed and simulated in LabVIEW-2020 workbench. From the results, it has been observed that the even and odd parity-based time complexity performance is appreciable in trade off with other performance parameters. The execution speed of modified parity modules gives an optimal solution in different situations to make automation process faster and reliable. From this study, new search-based strategies can be developed and implemented in automation sector for betterment.