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Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem

Białogłowski M., Staniaszek M., Laskowski W., Grudniak M.

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

2018

nr 19

7--24

We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.