Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25372
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Hernando, Leticia
Daolio, Fabio
Veerapen, Nadarajen
Ochoa, Gabriela
Contact Email: nve@cs.stir.ac.uk
Title: Local Optima Networks for the Permutation Flowshop Scheduling Problem: Makespan vs. Total Flow Time
Citation: Hernando L, Daolio F, Veerapen N & Ochoa G (2017) Local Optima Networks for the Permutation Flowshop Scheduling Problem: Makespan vs. Total Flow Time In: 2017 IEEE Congress on Evolutionary Computation (CEC), Piscataway, NJ, USA: IEEE. IEEE Congress on Evolutionary Computation 2017, 5.6.2017 - 8.6.2017, San Sebastián, Spain, pp. 1964-1971.
Issue Date: 7-Jul-2017
Conference Name: IEEE Congress on Evolutionary Computation 2017
Conference Dates: 2017-06-05T00:00:00Z
Conference Location: San Sebastián, Spain
Abstract: Local Optima Networks were proposed to understand the structure of combinatorial landscapes at a coarse-grained level. We consider a compressed variant of such networks with features that are meaningful for the study of search difficulty in the context of local search. In particular, we investigate different landscapes of the Permutation Flowshop Scheduling Problem. The insert and 2-exchange neighbourhoods are considered, and two different objective functions are taken into account: the makespan and the total flow time. The aim is to analyse the network features in order to find differences between the landscape structures, giving insights about which features impact algorithm performance. We evaluate the correlation between landscape properties and the performance of an Iterated Local Search algorithm. Visualisation of the network structure is also given, where evident differences between the makespan and total flow time are observed.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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