Solution Representation Learning in Multi-Objective Transfer Evolutionary Optimization

Ray Lim

doi:10.1109/ACCESS.2021.3065741

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Solution Representation Learning in Multi-Objective Transfer Evolutionary Optimization

2021-01-01

DOI：10.1109/ACCESS.2021.3065741

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Abstract

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This paper presents a first study on solution representation learning for inducing greater alignment and hence positive transfers between distinct multi-objective optimization tasks that bear discrepancies in their original search spaces. We first establish a novel probabilistic model-based multi-objective transfer evolutionary optimization (TrEO) framework with solution representation learning, capable of activating positive transfers while simultaneously curbing the threat of negative transfers. In particular, well-aligned solution representations are learned via spatial transformations to handle mismatches in search space dimensionalities between distinct multi-objective problems, as well as to increase the overlap between their optimized search distributions. We then showcase different algorithmic instantiations and case studies of the proposed framework in applications spanning continuous as well as discrete optimization; illustrative examples include multi-objective engineering design and route planning of unmanned aerial vehicles. The experimental results show that our framework helps induce positive transfers by unveiling useful but hidden inter-task relationships, thus bringing about faster search convergence to solutions of high quality in multi-objective TrEO.

Keywords:

Optimization

Task analysis

Probabilistic logic

Search problems

Sociology

Adaptation models

Mixture models

Transfer evolutionary optimization

solution representation learning

multi-objective optimization

probabilistic model-based search