Process optimization using genetic algorithm

Learning fuzzy rule-based systems with genetic algorithms can lead to very useful descriptions of several optimization and search problems. In the fuzzy logic method, when the inputs to the fuzzy controller in any process are increased, then the number of rules increases exponentially. To overcome the above problem, genetic algorithm (GA) is used. Genetic algorithm is a search and optimization technique which uses the probabilistic approach for rule optimization in non-linear controller. The technique is used to reduce the number of rules and to produce an optimal solution. Steel-making process is chosen as multiple input single output (MISO) process, in which different clustering methods are applied to analyze their performances. The comparison proves the superiority of GA based fuzzy clustering algorithm, which minimizes the rules in the rule base by eliminating the redundant rules and minimizing the volume of clusters thereby reducing the computational time.