A Modified Ant Colony Optimizer to Solve a Variant of Dynamic TSP

Author(s):

Prof. Archana M. Nayak , GIDC Degree Engineering College, Navsari; Prof. Kaushik S. Patel, GIDC Degree Engineering College, Navsari; Prof. Kaushal T. Kevadia, GIDC Degree Engineering College, Navsari; Prof. Brijesh U. Patel, GIDC Degree Engineering College, Navsari

Keywords:

Ant Colony Optimizer (ACO), Combinatorial Optimization, Dynamic TSP, Hamilton tour, Pheromone Values

Abstract:

Combinatorial Optimization Algorithms solve small or large instances of problems which cannot be solved in some polynomial time. These algorithms solve these types of problems by decreasing the actual size of search space. Search space consists of populations of possible solutions for respective problem. The problem considered here is generally known as NP-hard problem. The Traveling Salesman Problem is a fit example of this type of problem. The ACO is a particular meta heuristic motivated by the performance of real ants. A significant behavior of ant colonies is their foraging behavior, that how ants can find shortest paths between food sources and their nest. In this paper we have proposed an Ant Colony Optimizer to solve a variant of Dynamic TSP. The variant of Dynamic TSP in which cities are inserted and deleted at run time is considered in this work.

Other Details:

Manuscript Id	:	IJSTEV2I11242
Published in	:	Volume : 2, Issue : 11
Publication Date	:	01/06/2016
Page(s)	:	631-635

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