APPLICATION OF GENETIC ALGORITHMS TO THE TRAVELING SALESMAN PROBLEM

The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows setting selected parameters of the evolutionary algorithm and solving the prepared problem. The results are presented in both text and graphical form. The correctness of the evolutionary algorithm's operation and the implementation was confirmed by performed tests. A large number of tested solutions (2500) and the analysis of the obtained results allowed for a conclusion that an optimal (relatively suboptimal) solution had been found

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Markov chain monte carlo and the traveling salesman problem.

by Liang Fa Ming.Publication date from spine.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 49-53).ABSTRACT --- p.1Chapter CHAPTER 1 : --- Introduction --- p.2Chapter 1.1 ： --- The TSP Problem --- p.2Chapter 1.2: --- Application --- p.3Chapter CHAPTER 2 : --- Review of Exact and Approximate Algorithms for TSP --- p.4Chapter 2.1 : --- Exact Algorithm --- p.4Chapter 2.2 : --- Heuristic Algorithms --- p.8Chapter CHAPTER 3 : --- Markov Chain Monte Carlo Methods --- p.16Chapter 3.1: --- Markov Chain Monte Carlo --- p.16Chapter 3.2 : --- Conditioning and Gibbs Sampler --- p.17Chapter 3.3: --- The Metropolis-Hasting Algorithm --- p.18Chapter 3.4: --- Auxiliary Variable Methods --- p.21Chapter CHAPTER 4: --- Weighted Markov Chain Monte Carlo Method --- p.24Chapter CHAPTER 5 : --- Traveling Salesman Problem --- p.31Chapter 5.1: --- Buildup Order --- p.33Chapter 5.2: --- Path Construction through a Group of Points --- p.34Chapter 5.3: --- Solving TSP Using the Weighted Markov Chain Method --- p.38Chapter 5.4: --- Temperature Scheme --- p.40Chapter 5.5 : --- How to Adjust the Constant Prior-Ratio --- p.41Chapter 5.6: --- Validation of Our Algorithm by a Simple Example --- p.41Chapter 5.7 : --- Adding/Deleting Blockwise --- p.42Chapter 5.8: --- The sequential Optimal Method and Post Optimization --- p.43Chapter 5. 9 : --- Composite Algorithm --- p.44Chapter 5.10: --- Numerical Comparisons and Tests --- p.45Chapter CHAPTER 6 : --- Conclusion --- p.48REFERENCES --- p.49APPENDIX A --- p.54APPENDIX B --- p.58APPENDIX C --- p.6