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Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks

Tian, Ye, Lu, Chang, Zhang, Xingyi, Tan, Kay Chen and Jin, Yaochu (2020) Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks IEEE Transactions on Cybernetics.

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Abstract

Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto optimal subspace is approximated during the evolutionary process, the search space can be reduced and the difficulty encountered by evolutionary algorithms can be highly alleviated. Following the above idea, this paper proposes an evolutionary algorithm to solve sparse LMOPs by learning the Pareto optimal subspace. The proposed algorithm uses two unsupervised neural networks, a restricted Boltzmann machine and a denoising autoencoder to learn a sparse distribution and a compact representation of the decision variables, where the combination of the learnt sparse distribution and compact representation is regarded as an approximation of the Pareto optimal subspace. The genetic operators are conducted in the learnt subspace, and the resultant offspring solutions then can be mapped back to the original search space by the two neural networks. According to the experimental results on eight benchmark problems and eight real-world problems,the proposed algorithm can effectively solve sparse LMOPs with 10,000 decision variables by only 100,000 evaluations.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Tian, Ye
Lu, Chang
Zhang, Xingyi
Tan, Kay Chen
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 7 March 2020
Uncontrolled Keywords : Large-scale multi-objective optimization, sparse Pareto optimal solutions, Pareto optimal subspace, restricted Boltzmann machine, denoising autoencoder
Depositing User : James Marshall
Date Deposited : 11 Mar 2020 14:39
Last Modified : 11 Mar 2020 14:39
URI: http://epubs.surrey.ac.uk/id/eprint/853908

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