An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop

Ahmed El-Bouri*

*المؤلف المقابل لهذا العمل

نتاج البحث: Conference contribution

6 اقتباسات (Scopus)

ملخص

This paper examines the effect of initial solutions on the performance of an iterated local search (ILS) algorithm for the permutation flowshop problem with the objective of minimizing total flowtime. An ILS algorithm is applied to a set of test problems, and in each separate trial the algorithm is started from an initial solution generated by one of six different methods. Experimental results indicate that initial solutions generated by a neural network are more effective in promoting the performance of the ILS algorithm towards better solutions. A modified version of the ILS algorithm, in which an initially restricted neighborhood search is gradually expanded with each iteration, is also proposed and tested. The results from this modified ILS compare very favorably with published results from a traditional ILS approach.

اللغة الأصليةEnglish
عنوان منشور المضيف2012 IEEE Congress on Evolutionary Computation, CEC 2012
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2012
الحدث2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
المدة: يونيو ١٠ ٢٠١٢يونيو ١٥ ٢٠١٢

سلسلة المنشورات

الاسم2012 IEEE Congress on Evolutionary Computation, CEC 2012

Other

Other2012 IEEE Congress on Evolutionary Computation, CEC 2012
الدولة/الإقليمAustralia
المدينةBrisbane, QLD
المدة٦/١٠/١٢٦/١٥/١٢

ASJC Scopus subject areas

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