Exploring Long-term Memory in Evolutionary Multi-objective Algorithms: A Case Study with NSGA-III

In the field of many-objective optimization, obtaining a dense solution set is a challenging task, mostly due to having hyper-surface nature of Pareto-front; which cannot be covered by commonly utilized population sizes. This is particularly vital in scenarios where innovization and informed decisio...

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Bibliographic Details
Published in:Conference proceedings - Canadian Conference on Electrical and Computer Engineering pp. 864 - 870
Main Authors: Poor, Masoud Kermani, Rahnamayan, Shahryar, Bidgoli, Azam Asilian, Ebrahimi, Mehran
Format: Conference Proceeding
Language:English
Published: IEEE 06.08.2024
Subjects:
Archive
Artificial neural networks
Benchmark testing
Decision making
Evolutionary computation
IGD
Knee points
Long-term memory
Many-objective optimization
NSGA-III
Optimization
Pareto-front coverage
Training
ISSN:2576-7046
Online Access:Get full text
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Summary:In the field of many-objective optimization, obtaining a dense solution set is a challenging task, mostly due to having hyper-surface nature of Pareto-front; which cannot be covered by commonly utilized population sizes. This is particularly vital in scenarios where innovization and informed decision-making are crucial. The challenge stems from the constraints imposed by population size limitations in evolutionary algorithms, which impede the efficient exploration of multiple solutions. A contributing factor to this issue is the lack of long-term memory in the well-known evolutionary algorithms to retain these solutions. On the contrary, the effective training of machine learning-assisted optimization or innovization relies on a substantial amount of data, which can be provided by preserving these valuable solutions. Moreover, long-term memory can play a significant role in expensive many-objective optimization, where the repetition of the optimization process is both costly and time-consuming, similar to training deep neural networks. The study focuses on NSGA-III equipped with long-term memory and assessing its performance across 16 benchmark problems, encompassing DTLZ1 to DTLZ7 and WFG1 to WFG9, considering scenarios with 3, 5, and 10 objectives. This paper explores the benefits of incorporating long-term memory in terms of the ultimate optimization outcomes, including the number of non-dominated solutions, knee points, and Inverted Generational Distance (IGD).
ISSN:2576-7046
DOI:10.1109/CCECE59415.2024.10667327