Advanced Seminar on Recent Metaheuristics
Topic: How to expand Genetic Algorithms to Hybrid Metaheuristics
Abstract:
In the real-world of scheduling systems, there are many combinatorial optimization problems (COPs) imposing on more complex structure, nonlinear constraints, multiple objectives and uncertainty. The COPs make the problem intractable to the traditional approaches because of NP-hard ones. As one of the most typical scheduling problems, Flexible Job shop Scheduling Problem (FJSP) is a generalization of the job shop and parallel machine environment, which provides closer real manufacturing and logistics systems.
In order to develop an efficient algorithm whose reasonable computational time for NP-hard COPs, we have to consider 1) quality of solution, 2) computational time and 3) effectiveness of the nondominated solutions for multi-objective COP. As a subset of metaheuristics, Genetic Algorithm (GA) is a generic population-based metaheuristic such as Memetic Algorithm (MA), Particle Swarm Optimization (PSO), and Estimation of Distribution Algorithm (EDA). GA is a very powerful and broadly applicable stochastic search and optimization technique which is effective for solving various NP hard problems. However, in order to expand traditional GA in the quality, computational time and effectiveness, we have to combine it with Fuzzy Logic Controller, PSO, EDA, TOPSIS and TLBO for creating a hybrid metaheuristics.
Lectures Schedule:
Day 1: Introducing Traditional Metaheuristics (15:30-17:00, Dec. 8th, 2021)
Basic GA and Hybrid GA will introduce with applications. Hybrid evolutionary optimization with learning will summarize several algorithms and applications for scheduling & logistics (IJPR-2018).
Day 2: Multiobjective Hybrid GA and MoEA-HSS algorithms (15:30-17:00, Dec. 9th, 2021)
MoHGA, MoHGA/TOPSIS (IEEE Tran. on Auto. Sci. & Eng.-2014), and MoEA-HSS (J. Intelligent Manuf.-2014) will introduce with applications of Reverse Logistics, FJSP-SDST, and PPSP model.
Day 3: Uncertain FJSP models by Hybrid GA & PSO (15:30-17:00, Dec. 15th, 2021)
Hybrid GA combined PSO will introduce for solving FJSP with uncertain processing time (IEEE Trans. on Semicon. Manuf.-2018). Hybrid cooperative co-evolution algorithm will introduce for Fuzzy-FJSP (IEEE Trans. on Fuzzy Systems-2019).
Day 4: Hybrid Metaheuristics by GA.PSO.TLBO Algorithms (15:30-17:00, Dec. 16th, 2021)
Expanding GA and PSO with TLBO to Hybrid Metaheuristics will introduce for Supply Chain network model (Proc. of Inter. Conf. on Comp. Sci & Comp. Intell.-2021).
Online Meeting ID:
2021/12/8 – 2021/12/9: 腾讯会议779-4679-7406
点击链接入会：https://meeting.tencent.com/dm/AkpnUlJuuD1w
2021/12/15 – 2021/12/16: 腾讯会议434-2984-0587
点击链接入会：https://meeting.tencent.com/dm/mdehwSELNE0h
About the Lecturer:
Google Scholar Citation: Mitsuo Gen.
Fuzzy Logic Systems Institute and Tokyo University of Science, Japan; gen@flsi.or.jp
Mitsuo Gen received his PhD in Engineering from Kogakuin Univ., PhD degree in Informatics from Kyoto Univ. and is Senior Research Scientist at FLSI and Visiting Prof. at TUS. He was faculties at Ashikaga Institute of Tech. for 1974-2003, at Waseda Univ. for 2003-2010. He was visiting faculties at Univ. of California at Berkeley for 1999.8-2000.3, Texas A&M Univ. for 2000.1-3 & 2000.8-9, Hanyang Univ. in S. Korea for 2010-2012 and National Tsing Hua Univ. in Taiwan for 2012-2014. His research field is Evolutionary Computation, Manufacturing Scheduling and Logistics Systems. He is a coauthor of the following books: Genetic Algorithms and Engineering Design, 1997 and Genetic Algorithms and Engineering Optimization, 2000, John Wiley & Sons, New York; Network Models and Optimization: Multiobjective Genetic Algorithm Approach, 2008 and Introduction to Evolutionary Algorithms, 2010, Springer, London.
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