Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Основные понятия

A novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems.

Аннотация

The article introduces CLMEA, a new algorithm for high-dimensional expensive multi-objective optimization problems. It combines classifier-assisted rank-based learning with local model-based evolutionary strategies to explore uncertain but informative spaces efficiently. The algorithm aims to improve convergence and diversity in solving complex optimization problems. Experimental results demonstrate the superior performance of CLMEA compared to existing algorithms on benchmark problems and real-world applications.

Статистика

"The maximum number of FEs is set to 300 for all benchmarks."
"The population size is set to 50."
"The termination criterion is the predefined FEs."

Цитаты

"The proposed algorithm shows better convergence and diversity performance than other state-of-the-art MOEAs."
"CLMEA makes full use of the uncertainty of solutions in the decision space and objective space."

Ключевые выводы из

Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

by Guodong Chen... в arxiv.org 03-19-2024

https://arxiv.org/pdf/2304.09444.pdf

Дополнительные вопросы

How can CLMEA be adapted for different types of optimization problems

CLMEA can be adapted for different types of optimization problems by adjusting the parameters and strategies used in the algorithm. For instance, for optimization problems with a larger number of objectives, the population size and sampling strategies can be modified to handle the increased complexity. Additionally, for optimization problems with different constraints or search spaces, the local search strategy in CLMEA can be tailored to explore specific regions efficiently. By customizing these aspects based on the characteristics of the problem at hand, CLMEA can be effectively applied to a wide range of optimization scenarios.

What are the potential limitations or drawbacks of using a classifier-assisted approach in evolutionary algorithms

One potential limitation of using a classifier-assisted approach in evolutionary algorithms is that it may introduce additional computational overhead. Training and utilizing classifiers require resources and time, which could impact the overall efficiency of the algorithm. Moreover, if not properly designed or trained, classifiers may introduce biases or inaccuracies that could affect decision-making during evolution. It is essential to carefully optimize and validate the classifier model to ensure its effectiveness in guiding evolutionary processes without introducing unnecessary complexities.

How might reinforcement learning techniques enhance the performance of CLMEA in future iterations

Reinforcement learning techniques have the potential to enhance the performance of CLMEA by providing adaptive learning capabilities during evolution. By integrating reinforcement learning into CLMEA, the algorithm can dynamically adjust its exploration-exploitation trade-off based on feedback from previous iterations. This adaptive behavior can help CLMEA adapt more effectively to changing environments or complex problem landscapes. Reinforcement learning techniques could also assist in optimizing hyperparameters or tuning key components within CLMEA for improved convergence and solution quality over time.