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Mathematical Problems in Engineering
Volume 2014, Article ID 423621, 14 pages
http://dx.doi.org/10.1155/2014/423621
Research Article

A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Received 26 October 2013; Revised 2 January 2014; Accepted 19 February 2014; Published 1 April 2014

Academic Editor: Kui Fu Chen

Copyright © 2014 Hui Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [11 citations]

The following is the list of published articles that have cited the current article.

  • Hui Lu, and Mengmeng Zhang, “Non-integrated algorithm based on EDA and Tabu Search for test task scheduling problem,” 2015 Ieee Autotestcon, pp. 261–268, . View at Publisher · View at Google Scholar
  • Hui Lu, Lijuan Yin, Xiaoteng Wang, Mengmeng Zhang, and Kefei Mao, “Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem,” Mathematical Problems in Engineering, vol. 2014, pp. 1–25, 2014. View at Publisher · View at Google Scholar
  • Aimin Zhou, Yuting Zhang, Guixu Zhang, and Wenyin Gong, “On neighborhood exploration and subproblem exploitation in decomposition based multiobjective evolutionary algorithms,” 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 1704–1711, 2015. View at Publisher · View at Google Scholar
  • Jinhua Shi, Hui Lu, and Kefei Mao, “Solving the Test Task Scheduling Problem with a Genetic Algorithm Based on the Scheme Choice Rule,” Advances in Swarm Intelligence, vol. 9713, pp. 19–27, 2016. View at Publisher · View at Google Scholar
  • Shouyong Jiang, and Shengxiang Yang, “An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts,” Ieee Transactions On Cybernetics, vol. 46, no. 2, pp. 421–437, 2016. View at Publisher · View at Google Scholar
  • Jingrui Zhang, Qinghui Tang, Po Li, Daxiang Deng, and Yalin Chen, “A modified MOEA/D approach to the solution of multi-objective optimal power flow problem,” Applied Soft Computing Journal, vol. 47, pp. 494–514, 2016. View at Publisher · View at Google Scholar
  • Xin Xu, Lijuan Yin, Hui Lu, and Mengmeng Zhang, “Dynamic multi-objective evolutionary algorithm based on decomposition for test task scheduling problem,” Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015, pp. 11–18, 2016. View at Publisher · View at Google Scholar
  • Zhongbao Zhou, Xianghui Liu, Helu Xiao, Shijian Wu, and Yueyue Liu, “A DEA-based MOEA/D algorithm for portfolio optimization,” Cluster Computing, 2018. View at Publisher · View at Google Scholar
  • Wei-Dong Liu, Na Qu, Zheng-Yuan Wang, and Hui-Li Jing, “An Optimization Solution to Armament Parallel Test Task Scheduling,” Binggong Xuebao/Acta Armamentarii, vol. 39, no. 2, pp. 399–404, 2018. View at Publisher · View at Google Scholar
  • Rongrong Zhou, Hui Lu, and Jinhua Shi, “A solution framework based on packet scheduling and dispatching rule for job-based scheduling problems,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10942, pp. 202–211, 2018. View at Publisher · View at Google Scholar
  • Mei-Xian Song, Yong Wang, Jun-Qing Li, Li Li, and Pei-Yong Duan, “Optimal chiller loading by MOEA/D for reducing energy consumption,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10954, pp. 759–768, 2018. View at Publisher · View at Google Scholar