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Soft Computing
Published in: Soft Computing 8/2015

01-08-2015 | Methodologies and Application

An evaluation of non-redundant objective sets based on the spatial similarity ratio

Authors: Juan Zou, Jinhua Zheng, Cheng Deng, Ruimin Shen

Published in: Soft Computing | Issue 8/2015

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Abstract

Multi-objective optimization is a challenging task in many disciplines. Although a number of algorithms have simplified this problem by degenerating redundant objectives into low-dimensional sets, there is currently no consensus method for evaluating their performance. In this paper, we propose an evaluation method that uses a spatial similarity ratio (SSR) to determine the quality of non-redundant objective sets (NRSs). We consider the reduction of all NRSs of three functions from 5D to 2D or 3D using our SSR-based method, and compare the results to those given by an inverted generational distance-based method. The results demonstrate that our method is more accurate, as it takes information from both the non-redundant and redundant objective sets into consideration. In addition, using the proposed SSR-based approach, no prior knowledge of the true Pareto set is required. Therefore, we can conclude that our SSR-based method is feasible for the assessment of non-redundant objective sets.
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Metadata
Title
An evaluation of non-redundant objective sets based on the spatial similarity ratio
Authors
Juan Zou
Jinhua Zheng
Cheng Deng
Ruimin Shen
Publication date
01-08-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 8/2015
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1409-3

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