JACIII Vol.21 No.2 pp. 284-292
doi: 10.20965/jaciii.2017.p0284


A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

Lu Chen*, Bin Xin*,**,***,†, and Jie Chen**,***

*School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing, China

**Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing, China

***State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing, China

Corresponding author

June 27, 2016
November 11, 2016
Online released:
March 15, 2017
March 20, 2017
interactive multi-objective optimization, evolutionary algorithms, indifference tradeoffs, normal vector approximation, most preferred solution

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

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Last updated on Jan. 19, 2018