A Comparison of Nature Inspired Heuristics on the Traveling Salesman Problem (original) (raw)

Experiments with Local Search Heuristics for the Traveling Salesman Problem

2010

Abstract. In this paper, the experiments with local search heuristic algorithms for the traveling salesman problem (TSP) are described. Since the ordinary local search heuristics very seldom yield solutions of high quality, we investigate the enhanced local search algorithms using the extended neighbourhood structures. We also examine the performance of the local search heuristics in an iterated local search paradigm based on the deterministic descent-random ascent methodology. Our new heuristic algorithms are tested on the symmetric TSP instances taken from the publicly available electronic library of the TSP instances (TSPLIB). The results from the experiments demonstrate that our heuristics appear to be superior to traditional types of local search algorithms.

Solving TSP Using Various Meta-Heuristic Algorithms

International Journal of Recent Contributions from Engineering, Science & IT (iJES), 2013

Real world problems like Travelling Salesman Problem (TSP) belong to NP-hard optimization problems which are difficult to solve using classical mathematical methods. Therefore, many alternate solutions have been developed to find the optimal solution in shortest possible time. Nature-inspired algorithms are one of the proposed solutions which are successful in finding the solutions that are very near to the optimal. In this paper, Classical TSP (CTSP) along with its variant Random TSP (RTSP) are solved using various meta-heuristic algorithms and their performance is compared on the basis of tour length. Results show that the Nature-inspired algorithms outperform both Traditional and Evolutionary algorithms and obtain optimal solutions.

Nature Inspired Metaheuristics Comparative Study to Solve Traveling Salesman Problem

Journal of engineering & management in industrial system, 2021

There are numerous optimization method to solve the traveling salesman problem, TSP. One of methods is metaheuristics which is the state of the art algorithm that can solve the large and complex problem. The metaheuristics method is categorized as an approximate method can produce near-optimal solution for complex problem and even optimal solution for small problem in far shorter time than exact method. During Covid19 pandemic, most companies are trying to run business in more efficient and effective way, no exception in transportation sector. In this research, three of well-known nature inspired population based metaheuristics algorithm: Ant Colony Optimization-ACO, Artificial Bee Colony-ABC and Particle Swarm Optimization-PSO are compared to solve the 29 destinations in F&B company by using Matlab program. The result of this study is ACO produces the shortest distance, 94 kilometers and is 12.77% more efficient than ABC and 20.21 more efficient than PSO methods; but in process time consideration, the ABC has the fastest time to reach the optimality than others eventhough ACO reach optimality at 276 iterations; ABC reach at 861 iterations, and PSO reach at 10,000 iterations. For the next research, these methods should be tested in larger example and compared with Exact algorithm.

On the Solutions to the Travelling Salesman Problem using Nature Inspired Computing Techniques

This paper attempts to bring forward various newly emerged natural computing techniques to a common platform. Six such techniques are compared among each other which have been used to solve a well known classical problem, the travelling salesman problem. The techniques discussed in this paper are Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA), Gravitational Search Algorithm (GSA), Intelligent Water Drops (IWD), and River Formation Dynamics (RFD). In the end, some important results have been tabularized.

A hybrid heuristic for the traveling salesman problem

IEEE Transactions on Evolutionary Computation, 2001

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. Local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13 509 cities show the efficacy of the symbiosis between the two heuristics.

A Performance Evaluation of Selected Heuristics for the Travelling Salesman Problem

One of the classical problems in graph theory, which still has no close practicable exact algorithmic solution, is the Travelling Salesman’s Problem. It is a NP complete problem whose solution space explodes exponentially as the number of nodes (cities) increases. Hence, recourse is often made to the use of heuristics to solve problems modeled after the travelling salesman problem. Heuristics have proved over the years to be very good feasible solution methods for solving combinatorial optimization problems such travelling salesman problem, vehicle routing problem, knapsack problem, graph coloring and so on. In this paper, ant colony optimization, genetic algorithm and simulated annealing were implemented and compared for solving some TSP instances. These three heuristics are widely used heuristics for solving combinatorial optimization problems.

Implementation analysis of efficient heuristic algorithms for the traveling salesman problem

Computers & Operations Research, 2006

The state-of-the-art of local search heuristics for the traveling salesman problem (TSP) is chiefly based on algorithms using the classical Lin-Kernighan (L-K) procedure and the Stem-and-Cycle (S&C) ejection chain method. Critical aspects of implementing these algorithms efficiently and effectively rely on taking advantage of special data structures and on maintaining appropriate candidate lists to store and update potentially available moves. We report the outcomes of an extensive series of tests on problems ranging from 1,000 to 1,000,000 nodes, showing that by intelligently exploiting elements of data structures and candidate lists routinely included in state-of-the-art TSP solution software, the S&C algorithm clearly outperforms all implementations of the Lin-Kernighan procedure. Moreover, these outcomes are achieved without the use of special tuning and implementation tricks that are incorporated into the leading versions of the L-K procedure to enhance their computational efficiency.

Solving a traveling salesman problem using meta-heuristics

IAES International Journal of Artificial Intelligence (IJ-AI)

In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effect...

Discrete cuckoo search algorithm for the travelling salesman problem

Neural Computing and Applications, 2014

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous Traveling Salesman Problem (TSP), a combinatorial optimisation problem classified as NP-hard. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the cuckoo bird breeding behaviour. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve the CS by reconstructing the population of the solutions and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed Discrete Cuckoo Search (DCS) is tested against a set of benchmark instances of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics.

IMPROVING LOCAL SEARCH FOR THE TRAVELING SALESMAN PROBLEM

The subject of this paper is the improving of local search for the traveling salesman problem (TSP). In particular, a so-called fast descent-random ascent (FDRA) strategy is proposed. The FDRA approach is based on the fast-modified 2-opt algorithm combined with certain perturbation (random ascent) procedures. The results obtained from the experiments demonstrate that the new improved local search strategy is better than the other local search algorithms. This approach may also be applied to other combinatorial optimization problems.