Visualization and Performance Metric in Many-Objective Optimization

Visualization of population in a high-dimensional objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms (MaOEAs). In this paper, a new visualization method is proposed. It maps individuals from...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 20; no. 3; pp. 386 - 402
Main Authors: Zhenan He, Yen, Gary G.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation
Approximation methods
Dominance
Evolutionary algorithms
Evolutionary design method
Heating
many-objective evolutionary algorithm
many-objective optimization problem
mapping
Measurement
Pareto optimality
Performance measurement
performance metric
Quality assessment
Shape
Sociology
Statistics
Visualization
performance metric
mapping
visualization
many-objective optimization problem (MaOP)
Many-objective evolutionary algorithm (MaOEA)
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Visualization of population in a high-dimensional objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms (MaOEAs). In this paper, a new visualization method is proposed. It maps individuals from a high-dimensional objective space into a 2-D polar coordinate plot while preserving Pareto dominance relationship, retaining shape and location of the Pareto front, and maintaining distribution of individuals. From it, a decision-maker can observe the evolution process, estimate location, range, and distribution of Pareto front, assess quality of the approximated front and tradeoff between objectives, and easily select preferred solutions. Furthermore, its applications can be scalable to any dimensions, handle a large number of individuals on front, and simultaneously visualize multiple fronts for comparison. Based on this visualization tool, a performance metric, named polar-metric, is designed. The convergence of the approximate front is measured by radial values of all population members on that front. Meanwhile, the diversity performance is mainly determined by niche count of each subregion in a high-dimensional objective space. Experimental results show that it can provide a comprehensive and reliable comparison among MaOEAs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2472283