This article explores the meta-heuristic approach called scatter
search, which is an evolutionary method that has recently been shown
to yield promising outcomes for solving combinatorial and nonlinear
optimization problems. Based on formulations originally proposed in
the 1960s for combining decision rules and problem constraints, this
method uses strategies for combining solution vectors that have
proved effective in a variety of problem areas. Scatter search can be
implemented in multiple ways, and offer numerous alternatives for
exploiting its fundamental ideas. We identify a general design and
illustrate the main features in the context of a classical integer
programming problem.
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