A Decision Making Approach for Multi-Objective Optimization Considering A Trade-off Method

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Published: Dec 9, 2017
DOI: https://doi.org/10.37936/ecti-cit.2017112.98907
Keywords:
Multi-Objective Optimization Preference-based Method Trade-off Method

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Tipwimol Sooktip
Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Thailand
Naruemon Wattanapongsakorn
Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Thailand

Abstract

In multi-objective optimization problem, a set of optimal solutions is obtained from an optimization algorithm. There are many trade-off optimal solutions. However, in practice, a decision maker or user only needs one or very few solutions for implementation. Moreover, these solutions are difficult to determine from a set of optimal solutions of complex system. Therefore, a trade-off method for multi-objective optimization is proposed for identifying the preferred solutions according to the decision maker’s preference. The preference is expressed by using the trade-off between any two objectives where the decision maker is willing to worsen in one objective value in order to gain improvement in the other objective value. The trade-off method is demonstrated by using well-known two-objective and three-objective benchmark problems. Furthermore, a system design problem with component allocation is also considered to illustrate the applicability of the proposed method. The results show that the trade-off method can be applied for solving practical problems to identify the final solution(s) and easy to use even when the decision maker lacks some knowledge or not an expert in the problem solving. The decision maker only gives his/her preference information.  Then, the corresponding optimal solutions will be obtained, accordingly.

Article Details

How to Cite
[1]
T. Sooktip and N. Wattanapongsakorn, “A Decision Making Approach for Multi-Objective Optimization Considering A Trade-off Method”, ECTI-CIT Transactions, vol. 11, no. 2, pp. 178–189, Dec. 2017.
Issue
Vol. 11 No. 2 (2017): ECTI Transaction on CIT (Nov 2017)
Section
Artificial Intelligence and Machine Learning (AI)
Author Biographies

Tipwimol Sooktip, Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Thailand

Computer Engineering Department, KMUTT

Ph.D. Student

Naruemon Wattanapongsakorn, Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Thailand

Computer Engineering Department,

Associate Professor

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