New methods for computing visibility graphs
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Computational Geometry: Theory and Applications
The guilty net for the traveling salesman problem
Computers and Operations Research - Special issue on neural networks and operations research
Computers and Operations Research
A new point-location algorithm and its practical efficiency: comparison with existing algorithms
ACM Transactions on Graphics (TOG)
A neural-network-based approach to the double traveling salesman problem
Neural Computation
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Planning Tours of Robotic Arms among Partitioned Goals
International Journal of Robotics Research
Information Sciences: an International Journal
Approximate solution of the multiple watchman routes problem with restricted visibility range
IEEE Transactions on Neural Networks
A Sensor Placement Algorithm for a Mobile Robot Inspection Planning
Journal of Intelligent and Robotic Systems
An efficient self-organizing map designed by genetic algorithms for the traveling salesman problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES_DECOMPOSE
IEEE Transactions on Neural Networks
Visiting convex regions in a polygonal map
Robotics and Autonomous Systems
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In this paper, two state-of-the-art algorithms for the Traveling Salesman Problem (TSP) are examined in the multi-goal path planning problem motivated by inspection planning in the polygonal domain W. Both algorithms are based on the self-organizing map (SOM) for which an application in W is not typical. The first is Somhom's algorithm, and the second is the Co-adaptive net. These algorithms are augmented by a simple approximation of the shortest path among obstacles in W. Moreover, the competitive and cooperative rules are modified by recent adaptation rules for the Euclidean TSP, and by proposed enhancements to improve the algorithms' performance in the non-Euclidean TSP. Based on the modifications, two new variants of the algorithms are proposed that reduce the required computational time of their predecessors by an order of magnitude, therefore making SOM more competitive with combinatorial heuristics. The results show how SOM approaches can be used in the polygonal domain so they can provide additional features over the classical combinatorial approaches based on the complete visibility graph.