Random-Key Cuckoo Search for the Travelling Salesman Problem

TL;DR

This paper introduces the Random-Key Cuckoo Search algorithm, a novel metaheuristic approach that effectively solves the NP-hard Travelling Salesman Problem by combining random-key encoding and Levy flights, outperforming some existing algorithms.

Contribution

The paper presents a new hybrid algorithm, RKCS, integrating random-key encoding with cuckoo search and Levy flights for improved TSP solutions.

Findings

RKCS outperforms some existing metaheuristics on benchmark TSP instances.

The use of random-key encoding facilitates the transition from continuous to combinatorial space.

Levy flights enhance the exploration capability of the algorithm.

Abstract

Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random key cuckoo search (RKCS) algorithm for solving the famous Travelling Salesman Problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Levy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.