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Solving Mixed Pareto-Lexicographic Multiobjective Optimization Problems : The Case of Priority Levels

This article concerns the study of mixed Pareto-lexicographic multiobjective optimization problems where the objectives must be partitioned in multiple priority levels (PLs). A PL is a group of objectives having the same importance in terms of optimization and subsequent decision making, while between PLs a lexicographic ordering exists. A naive approach would be to define a multilevel dominance relationship and apply a standard EMO/EMaO algorithm, but the concept does not conform to a stable optimization process as the resulting dominance relationship violates the transitive property needed to achieve consistent comparisons. To overcome this, we present a novel approach that merges a custom nondominance relation with the Grossone methodology, a mathematical framework to handle infinite and infinitesimal quantities. The proposed method is implemented on a popular multiobjective optimization algorithm (NSGA-II), deriving a generalization of it called by us PL-NSGA-II. We also demonstrate the usability of our strategy by quantitatively comparing the results obtained by PL-NSGA-II against other priority and nonpriority-based approaches. Among the test cases, we include two real-world applications: one 10-objective aircraft design problem and one 3-objective crash safety vehicle design task. The obtained results show that PL-NSGA-II is more suited to solve lexicographical many-objective problems than the general purpose EMaO algorithms.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.5 October 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 971-985

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

GENETIC ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY COMPUTATION
LEXICOGRAPHIC OPTIMIZATION
GROSSONE METHODOLOGY (GM)
NUMERICAL INFINITESIMALS

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3068816

Pernyataan Tanggungjawab

Leonardo Lai, Lorenzo Fiaschi, Marco Cococcioni, Kalyanmoy Deb

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