Can the Agent with Limited Information Solve Travelling Salesman Problem?

Tomoko Sakiyama,Ikuo Arizono

doi:10.1155/2017/9562125

Abstract

Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.

Highlights

Travelling Salesman Problem (TSP) is well-known as one of the combinational optimization problems of finding the best solution out of a finite set of possible solutions
We can see that agents in heuristic inspired by real systems of animate beings do not need high ability of spatial cognition
Complexity generates an action that is required for not a short-term span but a long-term span [13]. With reference to such an action of animate beings, we developed a heuristic algorithm for solving TSP, in which individuals occasionally tuned their rules based on local environments while they basically obeyed the nearest neighbor (NN) method

Summary

Introduction

Travelling Salesman Problem (TSP) is well-known as one of the combinational optimization problems of finding the best solution out of a finite set of possible solutions. For example, TSP, there is heuristics as optimization algorithms inspired by real systems of animate beings [1] It appears that, in such optimization algorithms, the advanced intelligence, such as sophisticated cognitive ability and predictive ability, is demanded [1,2,3]. In such optimization algorithms, the advanced intelligence, such as sophisticated cognitive ability and predictive ability, is demanded [1,2,3] Real organisms such as insects and cells appear to solve complicate optimization problems by using only simple rules [4, 5]. This fact implies that real organisms do not need high ability of spatial cognition for solving complicate optimization problems. We can see that agents in heuristic inspired by real systems of animate beings do not need high ability of spatial cognition

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Conclusion