Enhancing Multi-Objective Optimization with Automatic Construction of Parallel Algorithm Portfolios

It has been widely observed that there exists no universal best Multi-Objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-Objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs ind...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 12; no. 22; p. 4639
Main Authors: Ma, Xiasheng, Liu, Shengcai, Hong, Wenjing
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Algorithms
Analysis
Business metrics
Design
Evolution & development
Evolutionary algorithms
Genetic algorithms
Investment analysis
Multiple objective analysis
Mutation
Optimization
Performance evaluation
Traveling salesman problem
Variables
Germany
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:It has been widely observed that there exists no universal best Multi-Objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-Objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to combine the advantages of different MOEAs. Since the manual construction of PAPs is non-trivial and tedious, we propose to automatically construct high-performance PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely MOEAs/PAP, which can better determine the output solution set for MOPs than conventional PAPs. Then, we present an automatic construction approach for MOEAs/PAP with a novel performance metric for evaluating the performance of MOEAs across multiple MOPs. Finally, we use the proposed approach to construct an MOEAs/PAP based on a training set of MOPs and an algorithm configuration space defined by several variants of NSGA-II. Experimental results show that the automatically constructed MOEAs/PAP can even rival the state-of-the-art multi-operator-based MOEAs designed by human experts, demonstrating the huge potential of the automatic construction of PAPs in multi-objective optimization.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12224639