Metaheuristics for bilevel optimization: A comprehensive review

Camacho-Vallejo, José-Fernando; Corpus, Carlos; Villegas, Juan G.

doi:10.1016/j.cor.2023.106410

Cited by 14 publications

(1 citation statement)

References 234 publications

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“…Optimization and metaheuristics stand at the forefront of innovative problem-solving across diverse fields, marking an era of significant evolution from traditional approaches to sophisticated strategies empowered by genetic algorithms and deep learning [ 21 , 22 ]. This relentless progression towards precision and efficiency has dramatically expanded the potential for discovering optimal solutions in complex landscapes [ 23 , 24 , 25 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

Caselli,

Soto,

Crawford

et al. 2023

Biomimetics

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In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term “autonomous” refers to these variants’ ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

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Section: Related Workmentioning

confidence: 99%

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

Caselli,

Soto,

Crawford

et al. 2023

Biomimetics

View full text Add to dashboard Cite

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A data‐driven Bayesian approach for optimal dynamic product transitions

Flores‐Tlacuahuac,

Fuentes‐Cortés

2024

AIChE Journal

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In the processing industry, dynamic product transitions are essential for achieving high product quality, minimizing the use of raw materials and energy and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this article, a data‐driven Bayesian approach for optimal dynamic product transitions is proposed. The proposed approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first‐principles dynamic mathematical model for drawing optimal solutions. The approach is applied to three case studies, and the results are comparable in performance quality with those obtained using a traditional gradient‐based optimization approach. The results show that the proposed approach is able to find optimal transition trajectories that meet the product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real‐world applications. The proposed approach has several advantages over traditional optimization approaches, including being data driven, able to cope with noisy measurements, computationally efficient, and it requires modest computational effort. Complex online optimal control problems can benefit from adopting a data‐driven Bayesian optimization scheme.

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