Fact Gathering Using Ant Colony Optimization

Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimization and particle swarm optimization which can be found in the literature. Designing fuzzy systems is an optimization problem. So in this paper, we introduce an Ant Colony optimization (ACO) approach to generate an optimized fuzzy rule base from available numerical data i.e. Fact Gathering. The ACO technique is inspired by real ant colony observations. It is a multi-agent approach to solving difficult combinatorial optimization problems. In the ACO meta-heuristic, artificial ant colonies cooperate in finding good solutions for difficult discrete optimization problems. Here, Ant paths help to determine the consequent parameters of generated rules.