Study on the impact of the NS in the performance of meta-heuristics in the TSP (original) (raw)
Related papers
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance
Journal of Mathematics
Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of t...
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 Hybrid Modified Meta-heuristic Algorithm for Solving the Traveling Salesman Problem
2016
The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony system (MACS) in each iteration, and at the second stage, the modified genetic algorithm (GA) and 2-opt local search are used for improving the solutions of the ants for that iteration. This process avoids the premature convergence and makes better solutions. Computational results on several standard instances of TSP show the efficiency of the proposed algorithm compared with the GA, ant colony optimization and other meta-heuristic algorithms.
2-opt based artificial bee colony algorithm for solving traveling salesman problem
Combinatorial optimization finds the best subset in a discrete problem space. In engineering or management fields, many problems are formulated as a combinatorial optimization problem. A well-known hard combinatorial optimization problem, the Traveling Salesman Problem (TSP), tries to find a minimum cost tour of n cities starting from a city, visiting all cities only once and finally returning the start city. An improvement algorithm, 2-Opt algorithm, is a basic local search heuristic for solving TSP. However, major disadvantages of 2-opt algorithm are that its performance is highly dependent upon the initial solution provided to it and it lacks a global search mechanism to escape local minima by uphill moves. Therefore, hybrid algorithms are needed to combine a global search heuristic with 2-opt local search algorithm. In this study, a hybrid method, called 2-Opt-ABC, is proposed which combines a successful global optimization algorithm, Artificial Bee Colony (ABC) algorithm, and 2-opt local search algorithm. In the method, ABC algorithm provides global search ability to avoid local minima and 2-opt algorithm improves local searches. Since 2-opt algorithm uses the improved solutions by ABC algorithm which uses neighborhood based 2-opt moves, the disadvantage of 2-opt algorithm, that is dependency of performance upon the quality of initial solutions, is removed. In the benchmarks, in order to investigate the combination of global search and local search, we have compared the performance of the 2-opt-ABC algorithm with that of 2-opt algorithm on some problem instances from TSPLIB.
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.
Optimal Routing in Traveling Salesman Problem using Artificial Bee Colony and Simulated Annealing
Traveling salesman problem (TSP) is a popular routing problem, which is a sub-problem of many application domains such as transportation, network communication and vehicle routing. TSP belongs to the class of NP-hard problems. Among many approaches which have been proposed for TSP, evolutionary and swarm intelligence algorithms efficiently applied to solve it, and attempt to avoid trapping in local minima. In this paper, a hybrid algorithm based on Artificial Bee Colony (ABC) and Simulated Annealing (SA) is proposed for TSP. The proposed algorithm, named ABCSA, is divided into two phases. The first phase searches globally the search space via population-based ABC algorithm. The second phase applies local-based SA algorithm in order to improve the final solution of ABC. Instead of a random solution, the final best solution gathered by ABC is considered as the initial solution of SA. To demonstrate the effectiveness and efficiency of the proposed algorithm, some benchmark problems from TSPLIB were tested and compared with the techniques of GA, PSO, SA and ABC. Results show that our algorithm achieved shorter distances in all cases with fewer generations and running time.
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)...
International Journal of Applied Information Systems, 2012
Metaheuristic algorithms have proved to be good solvers for the traveling salesman problem (TSP). All metaheuristics usually encounter problems on which they perform poorly so the programmer must gain experience on which optimizers work well in different classes of problems. However due to the unique functionality of each type of meta-heuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this paper, solution to the traveling salesman problem was implemented using genetic algorithm and simulated annealing. These algorithms were compared based on performance and results using several benchmarks. It was observed that Simulated Annealing runs faster than Genetic Algorithm and runtime of Genetic Algorithm increases exponentially with number of cities. However, in terms of solution quality Genetic Algorithm is better than Simulated Annealing.
Efficiency of selected meta-heuristics applied to the TSP problem: a simulation study
2003
Abstract: The paper presents a simulation study of the usefulness of a number of meta-heuristics used as optimisation methods for TSP problems. The five considered approaches are outlined: Genetic Algorithm, Simulated Annealing, Ant Colony System, Tabu Search and Hopfield Neural Network. Using a purpose-developed computer program, efficiency of the meta-heuritics has been studied and compared. Results obtained from about 40000 simulation runs are briefly presented and discussed.
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...