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Diversity Assessment in Many-Objective Optimization

Wang, H, Jin, Y and Yao, X (2016) Diversity Assessment in Many-Objective Optimization IEEE Transactions on Cybernetics.

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Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for manyobjective optimization, which is an accumulation of the dissimilarity in the population, where an Lp-norm-based (p < 1) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization.

Item Type: Article
Subjects : subj_Computing
Divisions : Surrey research (other units)
Authors : Wang, H, Jin, Y and Yao, X
Date : 19 May 2016
Funders : EPSRC
DOI : 10.1109/TCYB.2016.2550502
Copyright Disclaimer : © 2016 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.
Uncontrolled Keywords : diversity, many-objective optimization, metric, evolutionary algorithm
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 05 May 2016 09:16
Last Modified : 24 Jan 2020 12:53

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