Investigating the Properties of Indicators and an Evolutionary Many-Objective Algorithm Using Promising Regions
This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solution evaluation among these indicators. Accordingly, a promising-region-based evolutionary many-objectiv...
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| Published in | IEEE transactions on evolutionary computation Vol. 25; no. 1; pp. 75 - 86 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solution evaluation among these indicators. Accordingly, a promising-region-based evolutionary many-objective algorithm with the ratio-based indicator is proposed. In our proposed algorithm, a promising region is identified in the objective space using the ratio-based indicator with infinite norm. Since the individuals outside the promising region are of poor quality, we can discard these solutions from the current population. To ensure the diversity of population, a strategy based on the parallel distance is introduced to select individuals in the promising region. In this strategy, all individuals in the promising region are projected vertically onto the normal plane so that crowded distances between them can be calculated. Afterward, two solutions with a smaller distance are selected from the candidate solutions each time, and the solution with the smaller indicator fitness value is removed from the current population. Empirical studies on various benchmark problems with 3-20 objectives show that the proposed algorithm performs competitively on all test problems. Compared with a number of other state-of-the-art evolutionary algorithms, the proposed algorithm is more robust on these problems with various Pareto fronts. |
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| AbstractList | This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solution evaluation among these indicators. Accordingly, a promising-region-based evolutionary many-objective algorithm with the ratio-based indicator is proposed. In our proposed algorithm, a promising region is identified in the objective space using the ratio-based indicator with infinite norm. Since the individuals outside the promising region are of poor quality, we can discard these solutions from the current population. To ensure the diversity of population, a strategy based on the parallel distance is introduced to select individuals in the promising region. In this strategy, all individuals in the promising region are projected vertically onto the normal plane so that crowded distances between them can be calculated. Afterward, two solutions with a smaller distance are selected from the candidate solutions each time, and the solution with the smaller indicator fitness value is removed from the current population. Empirical studies on various benchmark problems with 3–20 objectives show that the proposed algorithm performs competitively on all test problems. Compared with a number of other state-of-the-art evolutionary algorithms, the proposed algorithm is more robust on these problems with various Pareto fronts. |
| Author | Zhang, Qingfu Gu, Fangqing Yuan, Jiawei He, Zhaoshui Liu, Hai-Lin |
| Author_xml | – sequence: 1 givenname: Jiawei orcidid: 0000-0001-5440-3794 surname: Yuan fullname: Yuan, Jiawei email: yuan.jiaweipaper@aliyun.com organization: School of automation, Guangdong University of Technology, Guangzhou, China – sequence: 2 givenname: Hai-Lin orcidid: 0000-0003-2276-1938 surname: Liu fullname: Liu, Hai-Lin email: hlliu@gdut.edu.cn organization: School of automation, Guangdong University of Technology, Guangzhou, China – sequence: 3 givenname: Fangqing surname: Gu fullname: Gu, Fangqing email: fqgu@gdut.edu.cn organization: School of automation, Guangdong University of Technology, Guangzhou, China – sequence: 4 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu email: qingfu.zhang@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 5 givenname: Zhaoshui orcidid: 0000-0001-5198-7851 surname: He fullname: He, Zhaoshui email: zhshhe@gdut.edu.cn organization: School of Automation and Guangdong Engineering Center of Auto-Detection Technology for Intelligent Manufacturing, Guangdong University of Technology, Guangzhou, China |
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| SubjectTerms | Algorithms Evolutionary algorithm Evolutionary algorithms Evolutionary computation Indicators Loss measurement many-objective optimization multiobjective optimization Multiple objective analysis Optimization Robustness Sociology Statistics Urban areas |
| Title | Investigating the Properties of Indicators and an Evolutionary Many-Objective Algorithm Using Promising Regions |
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