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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform

Xilu Wang¹, Yaochu Jin^2,3, Sebastian Schmitt⁴

¹Department of Computer Science, University of Surrey, Guildford, GU2 7XH, United Kingdom xilu.wang@surrey.ac.uk.

Evolutionary Computation

|November 5, 2021

Summary

This study introduces a novel transfer learning approach for multiobjective evolutionary algorithms (MOEAs) to handle objectives with varying evaluation times. The proposed method efficiently solves complex optimization problems by leveraging knowledge transfer between fast and slow objectives.

Keywords:

Bayesian optimization Gaussian process Multiobjective optimization co-surrogate non-uniform evaluation times surrogate-assisted evolutionary algorithm transfer learning

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Area of Science:

Computational intelligence
Optimization algorithms
Machine learning applications

Background:

Multiobjective evolutionary algorithms (MOEAs) often assume uniform objective evaluation times, which is unrealistic for many real-world problems.
Divergent computational costs across objectives hinder the efficiency of standard MOEAs in complex simulations or experiments.

Purpose of the Study:

To develop an efficient MOEA capable of handling objectives with non-uniform evaluation times.
To address the challenge of differing time complexities in objective function evaluations within evolutionary computation.

Main Methods:

A transfer learning scheme is proposed, integrating surrogate-assisted evolutionary algorithms (SAEAs).
A co-surrogate models the relationship between fast and slow objectives.
A transferable instance selection method facilitates knowledge acquisition from faster objective searches.

Main Results:

Experimental results on DTLZ and UF test suites validate the algorithm's performance.
The proposed algorithm demonstrates competitiveness in solving bi-objective optimization problems with non-uniform objective evaluation times.

Conclusions:

The developed transfer learning approach effectively manages MOEAs with objectives of varying computational costs.
This method offers a viable solution for real-world optimization scenarios characterized by disparate objective evaluation times.