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    An Iterated Local Search Approach for Finding Provably Good Solutions for Very Large TSP Instances

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    Abstract. Meta-heuristics usually lack any kind of performance guar-antee and therefore one cannot be certain whether the resulting solutions are (near) optimum solutions or not without relying on additional algo-rithms for providing lower bounds (in case of minimization). In this paper, we present a highly effective hybrid evolutionary local search algorithm based on the iterated Lin-Kernighan heuristic combined with a lower bound heuristic utilizing 1-trees. Since both upper and lower bounds are improved over time, the gap between the two bounds is minimized by means of effective heuristics. In experiments, we show that the proposed approach is capable of finding short tours with a gap of 0.8 % or less for TSP instances up to 10 million cities. Hence, to the best of our knowledge, we present the first evolutionary algorithm and meta-heuristic in general that delivers provably good solutions and is highly scalable with the problem size. We show that our approach outperforms all existing heuristics for very large TSP instances.
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