Increasing diversity and quality of students in Teaching-learning-based optimization algorithm to solve job shop scheduling problems

カテゴリ: 部門大会

論文No: SS1-7

グループ名: 【C】平成28年電気学会電子・情報・システム部門大会講演論文集

発行日: 2016/08/31

タイトル(英語): Increasing diversity and quality of students in Teaching-learning-based optimization algorithm to solve job shop scheduling problems

著者名: Li Linna(Waseda University),Weng Wei(Waseda University),Fujimura Shigeru(Waseda University)

著者名(英語): Linna Li|Wei Weng|Shigeru Fujimura

キーワード: teaching-learning-based|optimization|algorithm|job shop scheduling|local search

要約(日本語): TLBO algorithm is used to solve job shop problems. In the original TLBO algorithm, there are only the teacher and learner phases. In the teacher phase, all the students learn from one teacher and in the learner phase carry out among the existing students. So it’s not easy to find the optimal solution. To increase the diversity, firstly we introduce the multi-learning method in the teacher phase; secondly we improve the learning method of the learning phase; thirdly, improve the decoding procedure; fourthly, we combine the TLBO algorithm with local search technology. To show the efficiency of the improved TLBO, the simulation results for benchmark problems are compared with results derived by the other algorithms.

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