Ant Colony algorithm for routing problem using rule-mining

The proposed work presented a modified MAX-MIN Ant System (MMAS) algorithm to solve the routing problem, in which known demand are supplied from a store house with parallel routes for new local search. Routing Problem is an optimization problem and solved to nearly optimum by heuristics. The objective of routing issues is to use a fleet of vehicles with specified capacity to serve a number of users with dissimilar demands at minimum cost, without violating the capacity and route length constraints. Many meta-heuristic approaches like Simulated Annealing (SA), Tabu Search (TS) and An Improved Ant Colony System (IACS) algorithm are compared for the performance result analysis. The well-founded theory of fuzzy sets is a special way to model the uncertainty. The rules in a fuzzy model contain a set of propositions, each of which restricts a fuzzy variable to a single fuzzy value by means of the predicate equivalency. That way, each rule covers a single fuzzy region of the fuzzy grid. The proposed system of this thesis extends this structure to provide more general fuzzy rules, covering the input space as much as possible. In order to do this, new predicates are considered and a Max-Min Ant System is proposed to learn such fuzzy rules.