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new results with scatter search applied to multiobjective combinatorial and nonlinear optimization problems

This paper introduces two variants of a multiple criteria scatter search to deal with nonlinear continuous and combinatorial problems, applying a tabu search approach as a diversification generator method. Frequency memory and another escape mechanism are used to diversify the search. A Pareto relation is applied in order to designate a subset of the best generated solutions to be reference solutions. A choice function called Kramer Choice is used to divide the reference solution in two subsets. Euclidean and Hamming distances are used as measures of dissimilarity in order to find diverse solutions to complement the subsets of high quality current Pareto solutions to be combined. Linear combination and path relinking are used as a combination methods. The performance of these approaches are evaluated on several test problems taken from the literature. Keywords: Multiple objectives, metaheuristics, tabu search, scatter search, nonlinear optimization.