Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem

2009 ◽  
Vol 199 (1) ◽  
pp. 98-110 ◽  
Author(s):  
Prasanna Balaprakash ◽  
Mauro Birattari ◽  
Thomas Stützle ◽  
Marco Dorigo
Keyword(s):  
Local Search ◽  
Sample Size ◽  
Importance Sampling ◽  
Traveling Salesman Problem ◽  
Traveling Salesman

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

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