4,243 research outputs found
Renormalization for Discrete Optimization
The renormalization group has proven to be a very powerful tool in physics
for treating systems with many length scales. Here we show how it can be
adapted to provide a new class of algorithms for discrete optimization. The
heart of our method uses renormalization and recursion, and these processes are
embedded in a genetic algorithm. The system is self-consistently optimized on
all scales, leading to a high probability of finding the ground state
configuration. To demonstrate the generality of such an approach, we perform
tests on traveling salesman and spin glass problems. The results show that our
``genetic renormalization algorithm'' is extremely powerful.Comment: 4 pages, no figur
A hybrid heuristic solving the traveling salesman problem
This paper presents a new hybrid heuristic for solving the Traveling Salesman Problem, The
algorithm is designed on the frame of a general optimization procedure which acts upon two steps,
iteratively. In first step of the global search, a feasible tour is constructed based on insertion approach.
In the second step the feasible tour found at the first step, is improved by a local search optimization
procedure. The second part of the paper presents the performances of the proposed heuristic algorithm, on
several test instances. The statistical analysis shows the effectiveness of the local search optimization
procedure, in the graphical representation.peer-reviewe
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