Hybrid Adaptive Operator Selection Framework that Learns from Offline and Online Optimization Experiences

Core Concepts

A hybrid framework that effectively combines offline and online experiences to dynamically and adaptively select promising search operators for meta-heuristics, outperforming state-of-the-art methods.

Abstract

The paper proposes a hybrid adaptive operator selection (AOS) framework that combines offline and online experiences to dynamically and adaptively select promising search operators for meta-heuristics. The framework consists of three main components:

A state-based AOS module that learns the mapping from optimization state to promising operator selection. It first learns from offline experiences of solving past problems, then continuously learns from online experiences during the current problem-solving process.

A stateless AOS module that learns only from online experiences with relatively low computational cost.

A decision policy that balances the use of the state-based and stateless AOS modules in an online manner.

The effectiveness of the proposed hybrid framework is validated through extensive experiments on 170 real-value optimization problems and 34 instances of a challenging combinatorial optimization problem (Capacitated Vehicle Routing Problem with Time Windows). The results show that the hybrid framework outperforms state-of-the-art AOS methods, including both state-based and stateless approaches. Ablation studies verify the unique contributions of each component in the framework.

Stats

The paper reports the following key metrics:

Performance comparison of the proposed hybrid framework and other AOS methods on 138 real-value optimization problems and 33 CVRPTW instances.
Performance comparison of the proposed hybrid framework and its variants on the training set of 32 real-value optimization problems and the CVRPTW R101 instance.

Quotes

"Learning from previous problem-solving experiences can help adjust algorithm components of meta-heuristics, e.g., adaptively selecting promising search operators, to achieve better optimisation performance."
"Learning from online experiences obtained during the ongoing problem-solving process is more instructive but highly restricted by limited computational resources."

Key Insights Distilled From

Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework

by Jiyuan Pei,J... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10252.pdf

Deeper Inquiries

How can the proposed hybrid framework be extended to handle dynamic optimization problems where the problem characteristics change over time

To extend the proposed hybrid framework to handle dynamic optimization problems where the problem characteristics change over time, several adjustments can be made. One approach is to incorporate a mechanism for continuous learning and adaptation within the framework. This can involve updating the offline experiences database with new information as the optimization process progresses. By continuously integrating new data and experiences, the framework can adapt to changing problem characteristics and make more informed decisions.
Additionally, the decision policy adjusting mechanism can be enhanced to dynamically adjust the balance between the state-based and stateless AOS modules based on real-time feedback from the optimization process. This feedback can include metrics such as convergence rate, solution quality, and exploration-exploitation trade-offs. By monitoring these metrics and adjusting the decision policy in response to changing problem dynamics, the framework can effectively handle dynamic optimization problems.
Furthermore, incorporating mechanisms for self-adaptation and self-tuning within the framework can enable it to autonomously adjust its strategies and parameters in real-time based on the evolving problem landscape. This adaptive capability can help the framework maintain optimal performance in dynamic optimization scenarios.

What are the potential limitations of the current decision policy adjusting mechanism, and how can it be further improved to better balance the state-based and stateless AOS modules

The current decision policy adjusting mechanism in the hybrid framework may have limitations in scenarios where the problem characteristics change rapidly or unpredictably. To improve the balancing of the state-based and stateless AOS modules, several enhancements can be considered:

Adaptive Learning Rates: Implementing adaptive learning rates for the decision policy adjusting mechanism can enable it to dynamically adjust the weights assigned to the state-based and stateless modules based on the rate of change in problem characteristics. This can help the framework respond more effectively to rapid changes in the optimization landscape.

Dynamic Thresholds: Introducing dynamic thresholds for triggering adjustments in the decision policy can help ensure timely responses to shifts in problem dynamics. By setting thresholds based on performance metrics or problem features, the framework can adapt its decision-making process proactively.

Reinforcement Learning: Utilizing reinforcement learning techniques to train the decision policy adjusting mechanism can enable it to learn optimal strategies for balancing the state-based and stateless modules in real-time. By learning from the feedback received during the optimization process, the mechanism can continuously improve its decision-making capabilities.

Ensemble Approaches: Implementing ensemble approaches that combine multiple decision policies or strategies can enhance the robustness and adaptability of the framework. By leveraging diverse decision-making mechanisms, the framework can better handle uncertainties and variations in problem characteristics.

By incorporating these enhancements, the decision policy adjusting mechanism can be further improved to maintain an optimal balance between the state-based and stateless AOS modules in dynamic optimization scenarios.

Can the ideas of the hybrid framework be applied to other algorithm configuration tasks beyond operator selection, such as parameter tuning or algorithm selection

The ideas of the hybrid framework can indeed be applied to other algorithm configuration tasks beyond operator selection, such as parameter tuning or algorithm selection. By leveraging a combination of offline and online experiences, along with a decision policy adjusting mechanism, the framework can adapt to various algorithm configuration tasks in a dynamic and efficient manner.
For parameter tuning, the framework can be extended to learn from historical parameter settings and their corresponding performance outcomes. By integrating online experiences during the optimization process, the framework can dynamically adjust parameter values based on real-time feedback, optimizing the algorithm's performance.
Similarly, for algorithm selection, the hybrid framework can utilize offline experiences from past algorithm performances and online experiences from the current optimization process to guide the selection of the most suitable algorithm for a given problem. The decision policy adjusting mechanism can be tailored to balance the selection of different algorithms based on their performance and adapt to changing problem characteristics.
Overall, the principles of leveraging offline and online experiences, along with a dynamic decision policy adjusting mechanism, can be applied to a wide range of algorithm configuration tasks to enhance optimization performance and adaptability.