scholarly journals A Water Flow-Like Algorithm for the Travelling Salesman Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Ayman Srour ◽  
Zulaiha Ali Othman ◽  
Abdul Razak Hamdan
Keyword(s):  
Combinatorial Optimization ◽  
Water Flow ◽  
Travelling Salesman Problem ◽  
Experimental Results ◽  
Travelling Salesman ◽  
Benchmark Datasets ◽  
Grouping Problem

The water flow-like algorithm (WFA) is a relatively new metaheuristic that performs well on the object grouping problem encountered in combinatorial optimization. This paper presents a WFA for solving the travelling salesman problem (TSP) as a graph-based problem. The performance of the WFA on the TSP is evaluated using 23 TSP benchmark datasets and by comparing it with previous algorithms. The experimental results show that the proposed WFA found better solutions in terms of the average solution and the percentage deviation of the average solution from the best-known solution.

scholarly journals GENETIC ALGORITHM FOR OPTIMIZING DISTRIBUTION WITH ROUTE RESTRICTION CONSTRAINT DUE TO TRAFFIC JAMS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 295-301
Author(s):  
N. Mouttaki ◽  
J. Benhra ◽  
G. Rguiga
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Classical Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Simple Method ◽  
Traffic Jams ◽  
Better Than

Abstract. The Travelling Salesman Problem (TSP) is a classical problem in combinatorial optimization that consists of finding the shortest tour through all cities such that the salesman visits each city only one time and returns to the starting city. Genetic algorithm is one of the powerful ways to solve problems of traveling salesman problem TSP. The current genetic algorithm aims to take in consideration the constraints happening during the execution of genetic algorithm, such as traffic jams when solving TSP. This program has two important contributions. First one is proposing simple method into taking in consideration an inconvenient route linked to traffic jams. The second one is the use of closeness strategy during the initialization step, which can accelerate the execution time of the algorithm.The results of the experiments show that the improved algorithm works better than some other algorithms. The conclusion ends the analysis with recommendations and future works.

scholarly journals A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

2019 ◽  
Vol 2 (3) ◽  
pp. 446-453
Author(s):  
Murat Karakoyun
Keyword(s):  
Shortest Path ◽  
Travelling Salesman Problem ◽  
Experimental Results ◽  
Hard Problem ◽  
Shuffled Frog Leaping Algorithm ◽  
Travelling Salesman ◽  
New Approach ◽  
Starting Point ◽  
Np Hard Problem ◽  
Shuffled Frog Leaping

The Travelling Salesman Problem (TSP), which is a combinatorial NP-hard problem, aims to find the shortest possible path while visiting all cities (only once) in a given list and returns to the starting point. In this paper, an approach, which is based on k-means clustering and Shuffled Frog Leaping Algorithm (SFLA), is used to solve the TSP. The proposed approach consists of three parts: separate the cities into k clusters, find the shortest path for each cluster and merge the clusters. Experimental results have shown that the algorithm get better results as the number of cluster increase for problems that have a large number of cities.

scholarly journals Travelling Salesman Problem Generalized Interval Arithmetic

2021 ◽  
Vol 9 (12) ◽  
pp. 2281-2284
Author(s):  
Priya Dharshini. A
Keyword(s):  
Combinatorial Optimization ◽  
Fuzzy Sets ◽  
Interval Arithmetic ◽  
Optimization Problem ◽  
Travelling Salesman Problem ◽  
Arithmetic Operation ◽  
Combinatorial Optimization Problem ◽  
Common Method ◽  
Travel Times ◽  
Travelling Salesman

Abstract: The travelling salesman problem is one of the famous combinatorial optimization problem and has been intensively studied in the last decades. We present a new extension of the basics problem, where travel times are specified as a range of possible values. Keywords: Fuzzy sets, Arithmetic operation on interval, least common method, travelling salesman problem.

Multiobjective Travelling Salesman Problem under Stochastic Environment

2014 ◽  
Vol 530-531 ◽  
pp. 512-516 ◽  
Author(s):  
Qi Fang He ◽  
Tie Zhu Wang ◽  
Lu Pan ◽  
Zu Tong Wang
Keyword(s):  
Combinatorial Optimization ◽  
Operations Research ◽  
Optimization Model ◽  
Travelling Salesman Problem ◽  
Research Community ◽  
Stochastic Environment ◽  
Travelling Salesman ◽  
Solution Approach ◽  
Efficient Route ◽  
Pareto Efficient

Travelling salesman problem is a fundamental combinatorial optimization model studied in the operations research community, and yet, there is surprisingly little literature that addresses stochastic uncertainties and multiple objectives in it simultaneously. This paper is devoted to a novel TSP variation called stochastic multiobjective TSP (SMOTSP) with random variables on the arc, and a new solution approach is proposed to obtain Pareto efficient route in it, whose validity is proved finally.

scholarly journals Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
The Other ◽  
Critical Properties ◽  
Ant System ◽  
Travelling Salesman ◽  
Time Limits ◽  
System Models ◽  
Benchmark Datasets

In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions.

scholarly journals Solving Travelling Salesman Problem by Using Optimization Algorithms

2018 ◽  
Vol 3 (1) ◽  
pp. 17 ◽  
Author(s):  
Suhair Saud ◽  
Halife Kodaz ◽  
İsmail Babaoğlu
Keyword(s):  
Particle Swarm Optimization ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Shuffled Frog Leaping

This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered  as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other.Keywords: Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimization

scholarly journals Can the Agent with Limited Information Solve Travelling Salesman Problem?

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
The Other ◽  
Time Dependent ◽  
Critical Properties ◽  
Limited Information ◽  
Travelling Salesman ◽  
Benchmark Data ◽  
Benchmark Datasets

Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.

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