The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view.
A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered.
TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance.
Table of Contents
- Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem
- Bio-inspired Algorithms for TSP and Generalized TSP
- Approaches to the Travelling Salesman Problem Using Evolutionary Computing Algorithms
- Particle Swarm Optimization Algorithm for the Traveling Salesman Problem
- A Modified Discrete Particle Swarm Optimization Algorithm for the Generalized Traveling Salesman Problem
- Solving TSP by Transiently Chaotic Neural Networks
- A Recurrent Neural Network to Traveling Salesman Problem
- Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme
- Niche Pseudo-Parallel Genetic Algorithms for Path Optimization of Autonomous Mobile Robot – A Specific Application of TSP
- The Symmetric Circulant Traveling Salesman Problem
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File size: 6.58 MB
Number of pages: 202