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.
|Name||Genetic and Evolutionary Computation Conference, GECCO '13|
|Conference||the 15th Annual Conference on Genetic and Evolutionary Computation|
|Period||6/07/13 → 10/07/13|
- Stochastic Pareto local search
- Scalarization functions
- Multi-objective quadratic assignment problems
- Instance generator