“MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization

This paper introduces a multiple criteria scatter search to deal with bounded constrained non-linear continuous vector optimization problems of high dimension, applying a MultiStart Tabu Search (TS) as a diversification generation method, each TS works with its own starting point, recency memory, and aspiration threshold. Frequency memory is used to diversify the search and it is shared between the TS. 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 function is used to divide the reference solutions in two subsets. The Euclidean distance is used as a measure of dissimilarity in order to find diverse solutions to be combined. Linear combinations of the reference solutions are used as a solution combination method. “Balls” in the decision space and the objective space are used to avoid duplications. Different tabu sets with different tabu tenures are employed in the scatter phase to enhance the diversity of the search. The performance of our approach is compared with Pareto-optimal frontiers and three other state-of-the-art MOEAs for a suite test problems taken from the literature.