Hardware Implementation of 2-Opt Local Search Algorithm for the Traveling Salesman Problem

2007 ◽  
Author(s):  
Ioannis Mavroidis ◽  
Ioannis Papaefstathiou ◽  
Dionisios Pnevmatikatos
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
Hardware Implementation ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Local Search Algorithm ◽  
The Traveling Salesman Problem

scholarly journals Solve traveling salesman problem using EMF-CE algorithm

2020 ◽  
Author(s):  
Meng Luo ◽  
Shiliang Gu
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
State Of The Art ◽  
Search Algorithm ◽  
Feedback Regulation ◽  
Critical Value ◽  
Traveling Salesman ◽  
Online Library ◽  
The Traveling Salesman Problem ◽  
Algorithm Implementation

<p>In this paper, a novel search algorithm that based on the Contraction-Expansion algorithm and integrated three operators Exchange, Move, and Flip (EMF-CE) is proposed for the traveling salesman problem (TSP). EMF-CE uses a negative exponent function to generate critical value as the feedback regulation of algorithm implementation. Also, combined Exchange Step, Move step with Flip step and constitute of more than twenty combinatorial optimizations of program elements. It has been shown that the integration of local search operators can significantly improve the performance of EMF-CE for TSPs. We test small and medium scale (51-1000 cities) TSPs were taken from the TSPLIB online library. The experimental results show the efficiency of the proposed EMF-CE for addressing TSPs compared to other state-of-the-art algorithms.</p>

scholarly journals Solve traveling salesman problem using EMF-CE algorithm

2020 ◽  
Author(s):  
Meng Luo ◽  
Shiliang Gu
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
State Of The Art ◽  
Search Algorithm ◽  
Feedback Regulation ◽  
Critical Value ◽  
Traveling Salesman ◽  
Online Library ◽  
The Traveling Salesman Problem ◽  
Algorithm Implementation

<p>In this paper, a novel search algorithm that based on the Contraction-Expansion algorithm and integrated three operators Exchange, Move, and Flip (EMF-CE) is proposed for the traveling salesman problem (TSP). EMF-CE uses a negative exponent function to generate critical value as the feedback regulation of algorithm implementation. Also, combined Exchange Step, Move step with Flip step and constitute of more than twenty combinatorial optimizations of program elements. It has been shown that the integration of local search operators can significantly improve the performance of EMF-CE for TSPs. We test small and medium scale (51-1000 cities) TSPs were taken from the TSPLIB online library. The experimental results show the efficiency of the proposed EMF-CE for addressing TSPs compared to other state-of-the-art algorithms.</p>

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

An Iterated Local Search Algorithm for the Pollution Traveling Salesman Problem

2018 ◽  
pp. 83-91 ◽  
Author(s):  
Valentina Cacchiani ◽  
Carlos Contreras-Bolton ◽  
John W. Escobar ◽  
Luis M. Escobar-Falcon ◽  
Rodrigo Linfati ◽  
...  
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Traveling Salesman ◽  
Local Search Algorithm

scholarly journals Solve traveling salesman problem using EMF-CE algorithm

2020 ◽  
Author(s):  
Meng Luo ◽  
Shiliang Gu
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
State Of The Art ◽  
Search Algorithm ◽  
Feedback Regulation ◽  
Critical Value ◽  
Traveling Salesman ◽  
Online Library ◽  
The Traveling Salesman Problem ◽  
Algorithm Implementation

<p>In this paper, a novel search algorithm that based on the Contraction-Expansion algorithm and integrated three operators Exchange, Move, and Flip (EMF-CE) is proposed for the traveling salesman problem (TSP). EMF-CE uses a negative exponent function to generate critical value as the feedback regulation of algorithm implementation. Also, combined Exchange Step, Move step with Flip step and constitute of more than twenty combinatorial optimizations of program elements. It has been shown that the integration of local search operators can significantly improve the performance of EMF-CE for TSPs. We test small and medium scale (51-1000 cities) TSPs were taken from the TSPLIB online library. The experimental results show the efficiency of the proposed EMF-CE for addressing TSPs compared to other state-of-the-art algorithms.</p>

Solution Attractor of Local Search System: A Method to Reduce Computational Complexity of the Traveling Salesman Problem

2020 ◽  
Author(s):  
Weiqi Li
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Traveling Salesman ◽  
Search System ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is presumably difficult to solve exactly using local search algorithms. It can be exactly solved by only one algorithm—the enumerative search algorithm. However, the scanning of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This chapter attempts to answer these questions. A local search algorithm is a discrete dynamical system, in which a search trajectory searches a part of the solution space and stops at a locally optimal point. A solution attractor of a local search system for the TSP is defined as a subset of the solution space that contains all locally optimal tours. The solution attractor concept gives us great insight into the computational complexity of the TSP. If we know where the solution attractor is located in the solution space, we simply completely search the solution attractor, rather than the entire solution space, to find the globally optimal tour. This chapter describes the solution attractor of local search system for the TSP and then presents a novel search system—the attractor-based search system—that can solve the TSP much efficiently with global optimality guarantee.

scholarly journals CUDA Accelerated 2-OPT Local Search for the Traveling Salesman Problem

2020 ◽  
Author(s):  
Donald Davendra ◽  
Magdalena Metlicka ◽  
Magdalena Bialic-Davendra
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
High Performance ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Processing Unit ◽  
Large Problem ◽  
Intractable Problems ◽  
Problem Instances ◽  
The Traveling Salesman Problem

This research involves the development of a compute unified device architecture (CUDA) accelerated 2-opt local search algorithm for the traveling salesman problem (TSP). As one of the fundamental mathematical approaches to solving the TSP problem, the time complexity has generally reduced its efficiency, especially for large problem instances. Graphic processing unit (GPU) programming, especially CUDA has become more mainstream in high-performance computing (HPC) approaches and has made many intractable problems at least reasonably solvable in acceptable time. This chapter describes two CUDA accelerated 2-opt algorithms developed to solve the asymmetric TSP problem. Three separate hardware configurations were used to test the developed algorithms, and the results validate that the execution time decreased significantly, especially for the large problem instances when deployed on the GPU.

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