Sequential Approximate Multiobjective Optimization Using Computational Intelligence

1. Front Matter

   Pages 1-14

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2. Basic Concepts of Multi-objective Optimization

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 1-15

3. Interactive Programming Methods for Multi-objective Optimization

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 17-43

4. Generation of Pareto Frontier by Genetic Algorithms

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 45-71

5. Multi-objective Optimization and Computational Intelligence

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 73-112

6. Sequential Approximate Optimization

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 113-149

7. Combining Aspiration Level Approach and SAMO

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 151-168

8. Engineering Applications

   • Hirotaka Nakayama, Yeboon Yun, Min Yoon

   Pages 169-183

9. Back Matter

   Pages 1-13

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Many kinds of practical problems such as engineering design, industrial m- agement and ?nancial investment have multiple objectives con?icting with eachother. Thoseproblemscanbeformulatedasmultiobjectiveoptimization. In multiobjective optimization, there does not necessarily a unique solution which minimizes (or maximizes) all objective functions. We usually face to the situation in which if we want to improve some of objectives, we have to give up other objectives. Finally, we pay much attention on how much to improve some of objectives and instead how much to give up others. This is called “trade-o?. ” Note that making trade-o? is a problem of value ju- ment of decision makers. One of main themes of multiobjective optimization is how to incorporate value judgment of decision makers into decision s- port systems. There are two major issues in value judgment (1) multiplicity of value judgment and (2) dynamics of value judgment. The multiplicity of value judgment is treated as trade-o? analysis in multiobjective optimi- tion. On the other hand, dynamics of value judgment is di?cult to treat. However, it is natural that decision makers change their value judgment even in decision making process, because they obtain new information during the process. Therefore, decision support systems are to be robust against the change of value judgment of decision makers. To this aim, interactive p- grammingmethodswhichsearchasolutionwhileelicitingpartialinformation on value judgment of decision makers have been developed. Those methods are required to perform ?exibly for decision makers’ attitude.

• Evolutionary Multi-Objective Optimization
• Machine Learning
• Meta-Modelling
• Optimal Design of Experiments
• algorithms
• genetic algorithms
• modeling
• multi-objective optimization
• optimization

From the reviews:

"The basic concepts of multiobjective optimization are included making the book self-contained. The performance of the presented methods is demonstrated by the results of applications to real world problems. … The book is aimed to researchers, practitioners in industries and students of graduate course and high grade of undergraduate course." (Antanas Žilinskas, Zentralblatt MATH, Vol. 1167, 2009)

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