July 31, 2025

A Dynamic Dimensionality Expansion-Based Evolutionary Algorithm for Constrained Many-Objective Optimization

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A new algorithm enhances feasibility and convergence in constrained many-objective optimization, indicating potential for improved performance.

The algorithm addresses constraint obstacles and the curse of dimensionality in constrained many-objective optimization.
It employs a dynamic dimensionality expansion mechanism to convert the problem into a manageable format.
The method includes advanced techniques for solution generation, enhancing convergence and feasibility.
Numerical experiments demonstrate significant improvements over existing algorithms in solving various constrained many-objective problems.

Jiawei Yuan (2025) studied this question.

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A new algorithm enhances feasibility and convergence in constrained many-objective optimization, indicating potential for improved performance.