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Math. Meth. Oper. Res. (2006) 63: 193194
DOI 10.1007/s00186-005-0051-3
Published online: 17 January 2006
Springer-Verlag 2006Over the last 20 years or so combinatorial optimization has flourished into an area
with an almost uncountable number of successful real-world applications. One
of the major drivers for developing methods being able to solve even large scale
optimization problems was and is the field of metaheuristics. While a proper definition of metaheuristics goes beyond local search based approaches these turned
out to be the most widely known and successful approaches. And once local search
is combined with randomness one arrives at Stochastic Local Search. The authors
describe them as follows: Many widely known and high-performance local search
algorithms make use of randomized choices in generating or selecting candidate
solutions for a given combinatorial problem instance. These algorithms are called
stochastic local search (SLS) algorithms, and they constitute one of the most successful and widely used approaches for solving hard combinatorial problems.
(Certainly there is a more formal definition given as well.) Naturally, at least once
randomized and appended or hybridized by a local search component, these include
a wealth of methods such as simulated...