A reinforcement learning approach for dynamic multi-objective optimization

Zou, Fei; Yen, Gary G.; Tang, Lixin; Wang, Chunfeng

doi:10.1016/j.ins.2020.08.101

Cited by 90 publications

(16 citation statements)

References 38 publications

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“…In reinforcement learning, the computer is unaware of the actions to perform and must instead learn which acts are most rewarding via trial and error. The reward or lack of reward (punishment) gotten from its actions are indicative of how close the agent is to fulfil its objective [63]. The concept of reinforcement learning is a Markov Decision Process as shown in Fig.…”

Section: Reinforcement Learningmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

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Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented.INDEX TERMS Machine learning, local path planning, autonomous underwater vehicle (AUV), real-time path planning, underwater.

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Section: Reinforcement Learningmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

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“…Zou et al [174] proposed to detect the severity of change by measuring the amount of change in the objective values of detectors, and then developed a reinforcement learning approach to respond to environmental changes according to the severity of change. The severity of change has three categories: slight, medium, and severe, which are considered three states in reinforcement leanring.…”

Section: Dynamics-based Approachesmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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“…For example, Brandi et al [30] proposed a reinforcement learning model to control the supply water temperature setpoint of a heating system and obtained promising results for an office building in an integrated simulation environment. Zou et al [31] used reinforcement learning to solve the dynamic multi-objective optimization problem, which was proven to be effective through the evaluation on a real-world problem.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

Zhang

Chen

Zeng

et al. 2022

IFS

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The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic.

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A reinforcement learning approach for dynamic multi-objective optimization

Cited by 90 publications

References 38 publications

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

Evolutionary Dynamic Multi-objective Optimisation: A Survey

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

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