Critical Review of Performance Indicators for Constructing Consistent Solution Set Evaluation Framework in Evolutionary Many-Objective Optimization

2016 Fiscal Year Final Research Report

• Project/Area Number: 26540128
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Soft computing
• Research Institution: Osaka Prefecture University
• Principal Investigator: Ishibuchi Hisao 大阪府立大学, 工学(系)研究科(研究院), 教授 (60193356)
• Research Collaborator: NOJIMA YUSUKE ; IMADA RYO ; DOI KEN ; SETOGUCHI YU ; TANIGAKI YUKI ; HIROYUKI MASUDA
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 進化計算 / 多目的最適化 / 多数目的最適化 / 非劣解集合 / 解集合評価 / 性能評価尺度

Outline of Final Research Achievements

In evolutionary multiobjective optimization (EMO), a set of non-dominated solutions is obtained to approximate the entire Pareto front of a multiobjective problem. High evaluation is assigned to the solution set when it is close to the Pareto front and covers the entire Pareto front. When the number of objectives is two or three, we can depict the solution set and the Pareto front in the objective space. Thus it is easy to visually evaluate the solution set. It is also easy to visually understand the physical meaning of each performance indicator. However, the visual evaluation of solution sets and the visual understanding of performance indicators are very difficult for many-objective problems with four or more objectives. In this study, we analyzed various difficulties related to the evaluation of solution sets of many-objective problems. Then we proposed a new performance indicator and a test problem generation mechanism for evolutionary many-objective optimization.

Free Research Field

計算知能