A Web-Based Egg-Quality Expert Advisory System using Rule Based and Ant Colony (ACO) Optimization Algorithms

This paper deals with the development of Web based online expert systems using Evolutionary Algorithms. An expert system is acomputer application that performs a task that would otherwise be performed by a human expert. Here one of the evolutionary algorithms (ACOAlgorithm) is considered to find a good match of symptoms in the database. In the present paper, Ant Colony Optimization1 (ACO) algorithmhas been taken as the base and the concept of optimization is included, so that the new algorithm mainly focuses on the determination of thequality of eggs in the poultry farms. At first, the symptoms provided by the user are processed by a rule based expert system for identifying thequality of the eggs. If the rules required for processing the data by the above are not present in the database, then the system automatically callsthe machine learning algorithm technique. As a whole, the system results good optimized solution for recognizing the quality and viruses if anyaffected to eggs in poultry farms. And corresponding treatments to the viruses may also be suggested to the users. This expert system is designedwith JSP as front end and MySQL as backend