讲座时间：2015年9月25日   14：00——14:50
讲座地点：化纤楼207
主讲人：Mitsuo Gen

Fuzzy Logic Systems Institute andTokyo University of Science, Japan 

Abstract:

Manycombinatorial optimization problems (COP) in real world engineeringsystems impose on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously and make the problem intractable to the traditional approaches because of NP-hard COP. In order to develop an efficient solution algorithm that is in a sense "good," that is, whose computational time is small, or at least reasonable for NP-hard combinatorial problems met in practice, we have to consider the

following issues:

- Quality of solution,

- Computational time and

- Effectiveness of the nondominated solutions for multiobjective optimization problem (MOP).

Evolutionaryalgorithm (EA)is a subset of evolutionary computation, a generic population-based metaheuristic such as genetic algorithm (GA),hybrid GA, particle swarm optimization (PSO), estimation of distribution algorithm (EDA)and Multiobjective GA. EAis based on principles from evolution theory, and it is very powerful and broadly applicable stochastic search and optimization technique whichis effective for solving various NP hard COP.This lecture will be introduced a thorough treatment ofGenetic Algorithms, Hybridized GA (HGA),MultiobjectiveGA (MOGA) and to solve various combinatorialoptimization problems in real world such as Semiconductor Final Test Sche****ng, Reentrant FlowshopSche****ng problem in HDD manufacturing system , Module Assembly Sche****ng problem in TFT-LCD manufacturing systems and Semiconductor Devices Manufacturing Sche****ng problem.