On the Hardness of the Quadratic Assignment Problem with Metaheuristics
Journal of Heuristics
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Instance generators and test suites for the multiobjective quadratic assignment problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Simultaneous use of different scalarizing functions in MOEA/D
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Improving the anytime behavior of two-phase local search
Annals of Mathematics and Artificial Intelligence
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In order to simplify optimization in many-objective search spaces, we propose the Cartesian product of scalarization functions to reduce the number of objectives of the search space. To achieve this, we design a stochastic Pareto local search algorithm and we demonstrate their use on examples of product functions. We test this algorithm on generated many-objective quadratic assignment instances with correlated flow matrices. The experimental tests show a superior performance for the local search algorithms using product functions instead of the standard scalarization functions. For instances with strong correlation between the flow matrices, product based algorithms have similar performance with the standard Pareto local search.