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Title: Managing Objective Archives for Solution Set Reduction in Many-Objective Optimization
As objectives increase in many-objective optimization (MaOO), often so do the number of non-dominated solutions, potentially resulting in solution sets with thousands of non-dominated solutions. Such a larger final solution set increases difficulty in visualization and decision-making. This raises the question: how can we reduce this large solution set to a more manageable size? In this paper, we present a new objective archive management (OAM) strategy that performs post-optimization solution set reduction to help the end-user make an informed decision without requiring expert knowledge of the field of MaOO. We create separate archives for each objective, selecting solutions based on their fitness as well as diversity criteria in both the objective and variable space. We can then look for solutions that belong to more than one archive to create a reduced final solution set. We apply OAM to NSGA-II and compare our approach to environmental selection finding that the obtained solution set has better hypervolume and spread. Furthermore, we compare results found by OAM-NSGA-II to NSGA-III and get competitive results. Additionally, we apply OAM to reduce the solutions found by NSGA-III and find that the selected solutions perform well in terms of overall fitness, successfully reducing the number of solutions.  more » « less
Award ID(s):
1664858
PAR ID:
10493696
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Symposium Series on Computational Intelligence
ISBN:
978-1-6654-3065-4
Page Range / eLocation ID:
1491 to 1496
Format(s):
Medium: X
Location:
Mexico City, Mexico
Sponsoring Org:
National Science Foundation
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