Shortest-Cycle Search Task

A Shortest-Cycle Search Task is a optimal-cycle search task that is a shortest-path search task (that requires a shortest cycle in a graph).

• Context:
  • It can range from being a Single-Agent Shortest Cycle Search to being a Multi-Agent Shortest Cycle Search.
• Example(s):
  • a Traveling Salesperson Problem (that actually involves salesperson).
  • It can (typicall) be an NP-Hard Task.
  • …
• Counter-Example(s):
  • a Longest Cycle Finding Task.
• See: Combinatorial Optimization Task, Graph Cycle.

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References

2013

• http://en.wikipedia.org/wiki/Travelling_salesman_problem
  • The travelling salesman problem (TSP) or travelling salesperson problem asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.

    The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved.

    The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as DNA sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. In many applications, additional constraints such as limited resources or time windows make the problem considerably harder. TSP is a special case of the travelling purchaser problem.

    In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. Thus, it is likely that the worst-case running time for any algorithm for the TSP increases exponentially with the number of cities.

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