Learning-based multi-objective optimization through ANN-assisted online
            <i>Innovization</i>

Mittal, Sukrit; Saxena, Dhish Kumar; Deb, Kalyanmoy

doi:10.1145/3377929.3389925

Cited by 10 publications

(1 citation statement)

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“…A learning-assisted optimization method called innovized progress is an alternative approach that uses machine-learning methods, such as an artificial neural network and a random forest [89,90]. This approach builds a model based on the search history and progresses the offspring solutions using the model.…”

Section: Approachesmentioning

confidence: 99%

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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Population‐based evolutionary algorithms are suitable for solving multi‐objective optimization problems involving multiple conflicting objectives. This is because a set of well‐distributed solutions can be obtained by a single run, which approximate the optimal tradeoff among the objectives. Over the past three decades, evolutionary multi‐objective optimization has been intensively studied and used in various real‐world applications. However, evolutionary multi‐objective optimization faces various difficulties as the number of objectives increases. The simultaneous optimization of more than three objectives, which is called many‐objective optimization, has attracted considerable research attention. This paper explains various difficulties in evolutionary many‐objective optimization, reviews representative approaches, and discusses their effects and limitations. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Approachesmentioning

confidence: 99%