An improved cuckoo search algorithm for semiconductor final testing scheduling

Zhengcai Cao, Chengran Lin, Mengchu Zhou, Ran Huang

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

6 Scopus citations

Abstract

This paper presents a cuckoo search algorithm to minimize makespan for a semiconductor final testing scheduling problem. Each solution is a two-part vector consisting of a machine assignment and an operation sequence. In each iteration, a parameter feedback control scheme based on reinforcement learning is proposed to balance the diversification and intensification of population, and a surrogate model is employed to reduce computational cost. According to the Rechenberg's 1/5 Criterion, reinforcement learning uses the proportion of beneficial mutation as feedback. As a result, the surrogate modeling only needs to evaluate the relative ranking of solutions. A heuristic approach based on the smallest position value rule and a modular function is proposed to convert continuous solutions obtained from Levy flight into discrete ones. The computational complexity analysis is presented, and various simulation experiments are performed to validate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages1040-1045
Number of pages6
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jan 12 2018
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2017-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
CountryChina
CityXi'an
Period8/20/178/23/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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