MAX-MIN ANT OPTIMIZER FOR PROBLEM OF UNCERTAINITY
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 3
Abstract
The real life problems deal with imperfectly specified nowledge and some degree of imprecision, uncertainty or nconsistency is embedded in the problem specification. The well-founded theory of fuzzy sets is a special way to model the uncertainty. The rules in a fuzzy model ntain a set of ropositions, each of which restricts a fuzzy riable to a single fuzzy value by means of the predicate quivalency. That way, each rule covers a single uzzy region of the fuzzy grid. The proposed system of this hesis extends this tructure to rovide more general fuzzy ules, covering the input space as much as ssible. In order to do this, new predicates are considered and a Max-Min Ant ystem is proposed to learn such fuzzy rules. Ant system is a eneral purpose algorithm inspired by the study of behavior of nt colonies. It is based on cooperative search paradigm that is applicable to the solution of combinatorial optimization roblem. In this thesis we consider the combinatorial ptimization issue of Travelling salesman problem (TSP) hich evaluates more generic Fuzzy rules provided by ax-Min Ant System (MMAS). The existing ant colony system ACS) was a distributed algorithm applied to the travelling alesman problem (TSP). In ACS, a set of cooperating agents alled ants cooperate to find good solutions for TSPs (but here, Ants search their path randomly). Ants cooperate using an indirect form of communication mediated by heromone they deposit on the edges of the TSP problem in ymmetric instances. However most of the TSP issues carry oth symmetric and asymmetric instances
Authors and Affiliations
Mr. K. Sankar , K. Krishnamoorthy
Keywords
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