Cartesian products of scalarization functions for many-objective QAP instances with correlated flow matrices

Madalina Drugan

Research output: Chapter in Book/Report/Conference proceedingConference paper

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO '13
Number of pages8
Publication statusPublished - Jul 2013
Eventthe 15th Annual Conference on Genetic and Evolutionary Computation - Amsterdam, Netherlands
Duration: 6 Jul 201310 Jul 2013

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO '13

Conference

Conferencethe 15th Annual Conference on Genetic and Evolutionary Computation
CountryNetherlands
CityAmsterdam
Period6/07/1310/07/13

Keywords

  • Stochastic Pareto local search
  • Scalarization functions
  • Multi-objective quadratic assignment problems
  • Instance generator

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