Group Search Optimization to solve Traveling Salesman Problem

Abstract

The goal of Traveling Salesman Problem (TSP) is to find the shortest circular tour visiting every city exactly once. TSP has many real world applications and a number of methods have been investigated to solve TSP. Recently, nature inspired algorithms are also attracted to solve it. Here we studied Group Search Optimizer(GSO), the recently proposed nature inspired algorithm, to solve TSP. GSO is a population based optimization technique on the metaphor of producer-scrounger based social behavior of animals where producer searches for finding foods and scrounger searches for joining opportunities. GSO has found as an efficient method for solving function optimization problems for which it modeled. In this study we employ the concept of Swap Operator (SO) and Swap Sequence (SS) to modify GSO for TSP. The modified GSO (mGSO) was tested on a number of benchmark TSPs and results compared with some existing approaches. mGSO has shown best results (best tour cost) for some problems and competitive performance in other cases.