Stochastic pareto local search for many objective quadratic assignment problem instances

M.M. Drugan

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

4 Citations (Scopus)

Abstract

Optimising in many-objective search spaces, i.e. search spaces with more than three objectives, is a challenging task. Scalarization functions transform the multi-objective search space into a single objective search space. In order to scale-up optimisation in many-objective search spaces, we use Cartesian product of scalarization functions or simpler product functions to reduce the number of objectives of the search space. Stochastic product local search (SprLS) uses product functions to evaluate solutions within a local search run with the goal of generating the entire Pareto front. To improve the performance of SprLS algorithms: 1) we pursuit a fixed set the product function that improves the most the performance of the algorithm, or 2) we adapt the direction of the component scalarization functions using solutions in the current (possible suboptimal) Pareto front. We compare the performance of local search algorithms on many-objective quadratic assignment instances with correlated flow matrices.
Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, 25-28 May 2015, Sendai, Japan,
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1754-1761
Number of pages8
ISBN (Electronic)978-1-4799-7492-4
ISBN (Print)978-1-4799-7493-1
DOIs
Publication statusPublished - 10 Sept 2015

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