A Learning-Based Iterated Local Search Algorithm for Solving the Traveling Salesman Problem

2021 ◽  
pp. 45-61
Author(s):  
Maryam Karimi-Mamaghan ◽  
Bastien Pasdeloup ◽  
Mehrdad Mohammadi ◽  
Patrick Meyer
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Traveling Salesman ◽  
Local Search Algorithm ◽  
The Traveling Salesman Problem

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>

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>

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>

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