Metaheuristic Optimization via Memory and Evolution

Scatter search is an evolutionary method that has proved highly effective in solving several classes of non-linear and combinatorial optimization problems. Proposed early 1970s as a primal counterpart to the dual surrogate constraint relaxation methods, scatter search has recently found a variety of applications in a metaheuristic context. Because both surrogate constraint methods and scatter search incorporate strategic principles that are shared with certain components of tabu search methods, scatter search provides a natural evolutionary framework for adaptive memory programming. The aim of this paper is to illustrate how scatter search can be effectively used for the solution of general permutation problems that involve the determination of optimal cycles (or circuits) in graph theory and combinatorial optimization. In evidence of the value of this method in solving constrained optimization problems, we identify a general design for solving vehicle routing problems that sets our approach apart from other evolutionary algorithms that have been proposed for various classes of this problem.