Adaptive Weighted Aggregation 2: More scalable AWA for multiobjective function optimization

Adaptive Weighted Aggregation (AWA) is a frame work of multi-starting optimization methods based on scalarization for solving multiobjective function optimization problems. It progressively generates new solutions to refine the approximation of the Pareto set or the Pareto front by the subdivision,...

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Bibliographic Details
Published in:2011 IEEE Congress of Evolutionary Computation (CEC) pp. 2375 - 2382
Main Authors: Hamada, Naoki, Nagata, Yuichi, Kobayashi, Shigenobu, Ono, Isao
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2011
Subjects:
Approximation methods
Computational efficiency
Face
Optimization methods
Scalability
Search problems
ISBN:1424478340, 9781424478347
ISSN:1089-778X
Online Access:Get full text
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Summary:Adaptive Weighted Aggregation (AWA) is a frame work of multi-starting optimization methods based on scalarization for solving multiobjective function optimization problems. It progressively generates new solutions to refine the approximation of the Pareto set or the Pareto front by the subdivision, and iteratively estimates the appropriate weight vector for scalarization in each search by the weight adaptation. Our recent study shows that AWA's solution set combinatorially increases for the number of objectives. In this paper, we propose a new subdivision and weight adaptation scheme of AWA to improve its scalability. Numerical experiments show the effectiveness of the proposed method.
ISBN:1424478340
9781424478347
ISSN:1089-778X
DOI:10.1109/CEC.2011.5949911