Journal
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
Volume 14, Issue 3, Pages 61-74Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCI.2019.2919398
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Funding
- National Natural Science Foundation of China [61672033, 61822301, 61876123, U1804262]
- State Key Laboratory of Synthetical Automation for Process Industries [PAL-N201805]
- Anhui Provincial Natural Science Foundation [1808085J06, 1908085QF271]
- National Key R&D Program of China [2017YFC0804003]
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X386]
- Shenzhen Peacock Plan [KQTD2016112514355531]
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Diversity preservation plays an important role in the design of multi-objective evolutionary algorithms, but the diversity performance assessment of these algorithms remains challenging. To address this issue, this paper proposes a performance metric and a multi-objective test suite for the diversity assessment of multiobjective evolutionary algorithms. The proposed metric assesses both the evenness and spread of a solution set by projecting it to a lower-dimensional hypercube and calculating the volume of the projected solution set. The proposed test suite contains eight benchmark problems, which pose stiff challenges for existing algorithms to obtain a diverse solution set. Experimental studies demonstrate that the proposed metric can assess the diversity of a solution set more precisely than existing ones, and the proposed test suite can be used to effectively distin-guish between algorithms with respect to their diversity performance.
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