This paper surveys the "neurally" inspired problemsolving approaches to the traveling salesman problem, namely, t h e Hopfield-Tank network, the elastic net, and the self-organizing map. The latest achievements in the neural network domain are repor ted and numerical comparisons are provided with the classical solution approaches of operations research. An extensive bibliography wi th more than...

The purpose of this study to analyze genetic algorithm (GA) and simulated an-nealing (SA) based approaches applied well-known Traveling Salesman Prob-lem (TSP). As a NP-Hard problem, the goal TSP is find shortest route possible travel all cities, given set cities distances between cities. In order solve problem achieve optimal solution, permutations need be checked, which gets exponentially lar...

With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesman problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a ...

In this paper, we consider differential approximability of the traveling salesman problem (TSP). The approximation ratio was proposed by Demange and Paschos in 1996 as an criterion that is invariant under affine transformation objective function. We show TSP 3/4-differential approximable, which improves currently best known bound $$3/4 -O(1/n)$$ due to Escoffier Monnot 2008, where n denotes num...

In this paper, we prompt a new multi-dimensional algoithm to solve the traveling salesman problem based on the ant colony optimization algorithm and genetic algorithm. Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most impo...

This paper considers a spatially separated wireless sensor network (SS-WSN), which consists of a number of isolated subnetworks that could be far away from each other in distance. We address the issue of using mobile mules to collect data from these sensor nodes. In such an environment, both data collection latency and network lifetime are critical issues. We model this problem as a bi-objectiv...

Congestion in large cities and populated areas is one of the major challenges in urban logistics, and should be addressed at different planning and operational levels. The Time Dependent Travelling Salesman Problem (TDTSP) is a generalization of the well known Traveling Salesman Problem (TSP) where the travel times are not assumed to be constant along the day. The motivation to consider the tim...

In addition to the classical heuristic algorithms of operations research there have also been several approaches based on artificial neural networks which solve the traveling salesman problem (TSP). Their efficiency, however, decreases as the problem size (number of cities) increases. An idea to reduce the complexity of a large-scale TSP instance is to decompose or partition it into smaller sub...

In this paper, we propose a probabilistic modeling clonal selection algorithm which combines the clonal selection algorithm (CSA) and the probabilistic modeling (PM) for traveling salesman problem (TSP). The clonal selection algorithm is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus, can initialize antibodies and maintain the p...

The Traveling Salesman Problem (TSP), first formulated as a mathematical problem in 1930, has been receiving continuous and growing attention in artificial intelligence, computational mathematics and optimization in recent years. TSP can be described as follows: Given a set of cities, and known distances between each pair of cities, the salesman has to find a shortest possible tour that visits ...