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Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems

Tian, Ye, Cheng, Ran, Zhang, Xingyi, Li, Miqing and Jin, Yaochu (2019) Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems IEEE Computational Intelligence Magazine.

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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 multi-objective 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 distinguish between algorithms with respect to their diversity performance.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Tian, Ye
Cheng, Ran
Zhang, Xingyi
Li, Miqing
Date : 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Clive Harris
Date Deposited : 13 May 2019 07:44
Last Modified : 13 May 2019 07:44

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