Study of genetic algorithm with reinforcement learning to solve the TSP

Liu, Fei; Zeng, Guangzhou

doi:10.1016/j.eswa.2008.08.026

Cited by 143 publications

(68 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The third column is the optimal solution. The 4 th and the 5 th columns are the best solutions found by [21] and the proposed algorithm, respectively. The 6 th column shows the error percentages of both algorithms and the last two columns are the CPU time in have been both algorithms in seconds.…”

Section: Computational Resultsmentioning

confidence: 99%

“…As the second evidence to approve the validity of SimSum1, several experiments were carried out on 16 big test problems derived from TSPLIB [34] which have more than 1000 cities (from 1000 to 2392 cities) and the results were compared to the one reported by [21] who used RMGA algorithm in their research. The results are shown in Table II.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Today, most researchers try to find better heuristics for solving a wide range of large-scale instances of the TSP. [21] used and improved genetic algorithm with reinforcement mutation called RMGA to solve the TSP and showed that the result quality of their algorithm is better than previous works. [22] used a branch and bound algorithm together with simulation technique to solve several TSPs.…”

Section: Travelling Salesman Problemmentioning

confidence: 99%

See 2 more Smart Citations

SimSum1: a general optimisation via simulation approach for 0-1 programming models

Azimi¹

2012

Int. j. simul. model.

View full text Add to dashboard Cite

The current study develops a new general heuristic approach to address a special class of combinatorial problems, efficiently. The approach combines discrete event simulation together with relaxation techniques to solve a special class of 0-1 programming models including a constraint that restricts the summation of all variables to be 1. Three well-known combinatorial problems -including such a constraint-such as Dynamic Facility Layout Problem (DFLP), Graph Labelling Problem (GLP) and Travelling Salesman Problem (TSP) have been addressed and could be solved efficiently by the proposed algorithm. Several experiments have been carried out to show the efficiency of the algorithm. The results show that the proposed algorithm can be used for several real world applications while according to the best knowledge of the author, at least for DFLP and GLP, it is the fastest algorithm which has been developed in the literature.

show abstract

Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Travelling Salesman Problemmentioning

confidence: 99%

See 1 more Smart Citation

SimSum1: a general optimisation via simulation approach for 0-1 programming models

Azimi¹

2012

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…Examples of pure metaheuristics for the TSP include Simulated Annealing (Kirkpatrick et al, 1983;Malek et al, 1989), Tabu Search (Malek, 1988;Malek et al, 1989;Tsubakitani and Evans, 1998a), Guided Local Search (Voudouris and Tsang, 1999), Jump Search (Tsubakitani and Evans, 1998b), Randomized Priority Search (DePuy, Moraga and Whitehouse, 2005), Greedy Heuristic with Regret (Hassin and Keinan, 2008), Genetic Algorithms (Jayalakshmi et al, 2001;Tsai et al, 2003;Albayrak and Allahverdi, 2011;Nagata and Soler, 2012), Evolutionary Algorithms (Liao et al, 2012), Ant Colony Optimization (Dorigo and Gambardella, 1997), Artificial Neural Networks (Leung et al, 2004;Li et al, 2009), Water Drops Algorithm (Alijla et al, 2014), Discrete Firefly Algorithm (Jati et al, 2013), Invasive Weed Optimization (Zhou et al, 2015), Gravitational Search (Dowlatshahi et al, 2014), and Membrane Algorithms (He et al, 2014). Examples of hybrid metaheuristics include Simulated Annealing with Learning (Lo and Hsu, 1998), Genetic Algorithm with Learning (Liu and Zeng, 2009), SelfOrganizing Neural Networks and Immune System (Masutti and de Castro, 2009), Genetic Algorithm and Local Search (Albayrak and Allahverdi, 2011), Genetic Algorithm and Ant Colony Optimization (Dong at al., 2012), Honey Bees Mating and GRASP (Marinakis et al, 2011), and Particle Swarm Optimization and Ant Colony Optimization (Elloumi et al, 2014).…”

Section: Heuristic Approaches and Methodsmentioning

confidence: 99%

A dual local search framework for combinatorial optimization problems with TSP application

Ouenniche

Ramaswamy

Gendreau

2017

Journal of the Operational Research Society

View full text Add to dashboard Cite

Abstract:In practice, solving realistically sized combinatorial optimization problems (COPs) to optimality is often too time-consuming to be affordable; therefore, heuristics are typically implemented within most applications software. A specific category of heuristics has attracted considerable attention, namely, local search methods. Most local search methods are primal in nature; that is, they start the search with a feasible solution and explore the feasible space for better feasible solutions. In this research, we propose a dual local search method and customise it to solve the traveling salesman problem (TSP); that is, a search method that starts with an infeasible solution, explores the dual space -each time reducing infeasibility, and lands in the primal space to deliver a feasible solution. The proposed design aims to replicate the designs of optimal solution methodologies in a heuristic way. To be more specific, we solve a combinatorial relaxation of a TSP formulation, design a neighborhood structure to repair such an infeasible starting solution, and improve components of intermediate dual solutions locally. Sample-based evidence along with statistically significant t-tests support the superiority of this dual design compared to its primal design counterpart.

show abstract

“…Li et al [23] presented an improved ACO method for solving the TSP. Liu and Zeng [24] presented a genetic algorithm with reinforcement learning to solve the TSP. Chien and Chen [25] presented a method for solving the TSP based on the parallelized genetic ACS.…”

Section: Related Workmentioning

confidence: 99%

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

Peker¹,

Sen²,

Kumru³

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and parameter optimization was taken from the Taguchi method. The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken.In addition to these, a variance analysis was undertaken in order to identify the effect values of the parameters on the system. Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

show abstract

Study of genetic algorithm with reinforcement learning to solve the TSP

Cited by 143 publications

References 13 publications

SimSum1: a general optimisation via simulation approach for 0-1 programming models

SimSum1: a general optimisation via simulation approach for 0-1 programming models

A dual local search framework for combinatorial optimization problems with TSP application

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

Contact Info

Product

Resources

About