Meta-RaPS: a simple and effective approach for solving the traveling salesman problem (original) (raw)

A Novel Metaheuristic for Travelling Salesman Problem

One of the well-known combinatorial optimization problems is travelling salesman problem (TSP). This problem is in the fields of logistics, transportation, and distribution. TSP is among the NP-hard problems, and many different metaheuristics are used to solve this problem in an acceptable time especially when the number of cities is high. In this paper, a new meta-heuristic is proposed to solve TSP which is based on new insight into network routing problems.

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...

The using of solver software and vehicle routing for the traveling salesman problem

2014

The traveling salesman problem (TSP) is one of the most studied problems in management science. Optimal approaches to solving traveling salesman problems are based on mathematical programming. But in reality, most TSP problems are not solved optimally. When the problem is so large that an optimal solution is impossible to obtain, or when approximate solutions are good enough, heuristics are applied. Two commonly used heuristics for the traveling salesman problem are the nearest neighbor procedure and the Clark and Wright savings heuristic. In this paper will be present using of the solver software and principles of TSP for optimal solution of vehicle routing for domestic bottled water and different juices in the different parts of the Republic of Macedonia.

A COMPARATIVE STUDY OF META‐HEURISTICS METHODS FOR TRAVELLING SALESMAN PROBLEM

The paper presents a simulation study of the usefulness of a number of meta-heuristics used as optimization method for traveling salesman problem. The three considered approaches are outlined: Neighborhood Search, Hill Climbing and Simulated Annealing. Using a purpose-developed computer program, efficiency of the meta-heuristics has been studied and compared. The modeling of the environment is achieved through specific UML diagrams representing the stages of analysis, design and implementation. Implementation of informatics system is realized in Java programming language.

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.

The multiple traveling salesman problem: an overview of formulations and solution procedures

The multiple traveling salesman problem (mTSP) is a generalization of the well-known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Moreover, the characteristics of the mTSP seem more appropriate for real-life applications, and it is also possible to extend the problem to a wide variety of vehicle routing problems (VRPs) by incorporating some additional side constraints. Although there exists a wide body of the literature for the TSP and the VRP, the mTSP has not received the same amount of attention. The purpose of this survey is to review the problem and its practical applications, to highlight some formulations and to describe exact and heuristic solution procedures proposed for this problem.

Traveling salesman problem heuristics: Leading methods, implementations and latest advances

European Journal of Operational Research, 2011

Heuristics for the traveling salesman problem (TSP) have made remarkable advances in recent years. We survey the leading methods and the special components responsible for their successful implementations, together with an experimental analysis of computational tests on a challenging and diverse set of symmetric and asymmetric TSP benchmark problems. The foremost algorithms are represented by two families, deriving from the Lin-Kernighan (LK) method and the stem-and-cycle (S&C) method. We show how these families can be conveniently viewed within a common ejection chain framework which sheds light on their similarities and differences, and gives clues about the nature of potential enhancements to today's best methods that may provide additional gains in solving large and difficult TSPs.

Heuristics and Meta-Heuristics optimization methods in solving Traveling Salesman Problem TSP

2020

In modern societies there are increasingly more often problems of various kinds, and tests are needed to solve them in experimental ways. Although, develop a mathematical model that closely matches the reality to solve a real life problem is very complicated, since many of these models might has to contain very large number of variables (as a heuristic model that optimizes problems solving results). Furthermore, these shows as difficult problems in controlling subjec-tive behaviours, so They are making it even more complicated than these models resemble reality (wrong solving model leads to a more complex level). The purpose of this research is the study of combinatorial optimization problems using approximate methods. In particular, this work focuses on the analysis of meta-heuristics algorithms based on history and population related to the solu-tion of Travelling Salesman Problem (TSP) like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA)...

TSP Solver: An integrated framework for solving traveling salesman problem consistent with TSPLIB

Fuzzy Systems and Data Mining III, 2017

The Traveling Salesman Problem (TSP) is the subject of study in operational research for more than three decades. The TSP problem is NP-complete. Consequently, many heuristic and metaheuristic algorithms have been developed to cope with the intractable nature of the problem. Although the problem is well-studied, lack of integrated software that harness the new computers' computational power and provide an easy comparison between heuristic algorithms are sensible. TSP solver is the state-of-the-art software that provides a common framework to compare the performance of different algorithms over TSPLIB library. The widespread use of TSP and its benchmark library means that yield improvement can be significant.