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

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Published on: December 9, 2012

A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective

Mengjiao Yu¹, Zheng Wang², Rui Dai¹

¹College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310015, China.

Scientific Reports

|August 13, 2023

Summary

This summary is machine-generated.

This study introduces a new algorithm for expensive multi-objective optimization problems (EMOPs) in high dimensions. The two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) improves efficiency and performance.

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Area of Science:

Optimization Algorithms
Computational Intelligence
Engineering Mathematics

Background:

Surrogate-assisted evolutionary algorithms (SAEAs) are popular for expensive multi-objective optimization problems (EMOPs).
Existing SAEAs struggle with high-dimensional EMOPs due to the large sample requirements for surrogate models.
This limitation hinders the application of SAEAs in complex, real-world scenarios.

Purpose of the Study:

To develop an efficient surrogate-assisted evolutionary algorithm for high-dimensional EMOPs.
To address the computational challenges posed by large training sample requirements in high-dimensional spaces.
To enhance the performance and applicability of SAEAs in complex optimization tasks.

Main Methods:

A novel two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) is proposed.
The RBF model is utilized to approximate objective functions.
A two-stage selection strategy and a novel archive updating strategy are employed to manage computational cost and improve efficiency.

Main Results:

The proposed TSDEA demonstrates promising performance in high-dimensional EMOPs.
TSDEA shows significant computational efficiency compared to existing state-of-the-art SAEAs.
Experimental results validate the effectiveness of the developed strategies for handling high-dimensional problems.

Conclusions:

TSDEA offers an effective solution for high-dimensional expensive multi-objective optimization problems.
The algorithm balances performance and computational efficiency, making it suitable for complex applications.
This work advances the field of surrogate-assisted evolutionary computation for challenging optimization tasks.