ADMM-based OPF Problem Against Cyber Attacks in Smart Grid

Xu, Jiangjiao; Li, Ké; Abusara, Mohammad; Zhang, Yan

doi:10.1109/smc52423.2021.9658699

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Things become even more challenging when the constraints are (partially) unobservable [80,81]. It is also worth noting that evolutionary computation and multi-objective optimization have been successfully applied to solve real-world problems, e.g., natural language processing [82], neural architecture search [83][84][85][86], robustness of neural networks [87][88][89][90][91][92], software engineering [93][94][95][96][97], smart grid management [2,98,99], communication networks [100][101][102][103], machine learning [104][105][106][107][108], and visualization [109]. Finally, the explainability of the policies of interest and their implications has rarely been discussed in the literature, representing another area for future investigation.…”

Section: Discussionmentioning

confidence: 99%

An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning

Liu

Zhou

Qiu

et al. 2022

Lecture Notes in Computer Science

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Multi-objective reinforcement learning (MORL) aims to find a set of high-performing and diverse policies that address trade-offs between multiple conflicting objectives. However, in practice, decision makers (DMs) often deploy only one or a limited number of trade-off policies. Providing too many diversified trade-off policies to the DM not only significantly increases their workload but also introduces noise in multi-criterion decision-making. With this in mind, we propose a humanin-the-loop policy optimization framework for preference-based MORL that interactively identifies policies of interest. Our method proactively learns the DM's implicit preference information without requiring any a priori knowledge, which is often unavailable in real-world black-box decision scenarios. The learned preference information is used to progressively guide policy optimization towards policies of interest. We evaluate our approach against three conventional MORL algorithms that do not consider preference information and four state-of-the-art preference-based MORL algorithms on two MORL environments for robot control and smart grid management. Experimental results fully demonstrate the effectiveness of our proposed method in comparison to the other peer algorithms.

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