Large-step markov chains for the TSP incorporating local search heuristics

Olivier Martin, Steve W Otto, Edward W Felten
<span title="">1992</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">Operations Research Letters</a> </i> &nbsp;
We consider a new class of optimization heuristics which combine local searches with stochastic sampling methods, allowing one to iterate local optimization heuristics. We have tested this on the Euclidean Traveling Salesman Problem, improving 3-opt by over 1.6% and Lin-Kernighan by 1.3%. I Introduction Given N cities labeled by i = 1, N , separated by distances d i j , the Traveling Salesman Problem (TSP) consists in finding the shortest tour, i.e., the shortest
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1016/0167-6377(92)90028-2</a> <a target="_blank" rel="external noopener" href="">fatcat:3gqgdwjkqvhlnir7zgpfbzpm2e</a> </span>
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