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1. Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

   https://doi.org/10.3390/electronics11030401  

   Hashir, Syed Muhammad; Gupta, Sabyasachi; Megson, Gavin; Aryafar, Ehsan; Camp, Joseph (February 2022, Electronics)

   In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems. 

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2. Pareto Optimization of Analog circuits using Reinforcement Learning

   https://doi.org/10.1145/3640463  

   NS, Karthik Somayaji; Li, Peng (January 2024, ACM Transactions on Design Automation of Electronic Systems)

   Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for the designer to optimize for multiple competing objectives which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to: (i) Extrapolate current techniques in Multi-Objective Reinforcement Learning (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context. 

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3. Objective Selection for Cancer Treatment: An Inverse Optimization Approach

   https://doi.org/10.1287/opre.2021.2192  

   Ajayi, Temitayo; Lee, Taewoo; Schaefer, Andrew J. (May 2022, Operations Research)

   In radiation therapy treatment plan optimization, selecting a set of clinical objectives that are tractable and parsimonious yet effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner’s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a nonconvex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose multiple solution approaches, including greedy heuristics and regularized problems and application-specific methods that use anatomical information of the patients. Our results show that the proposed heuristics find objectives that are near optimal. Via curve analysis on dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences. 

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4. IMO^3: Interactive Multi-Objective Off-Policy Optimization

   https://doi.org/10.24963/ijcai.2022/489  

   Wang, Nan; Wang, Hongning; Karimzadehgan, Maryam; Kveton, Branislav; Boutilier, Craig (July 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22)

   Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose Interactive Multi-Objective Off-policy Optimization (IMO^3). The key idea of IMO^3 is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO^3 identifies a near-optimal policy with high probability, depending on the amount of designer's feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems. 

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5. Unified Distributed Control of Grid-Forming and Grid-Feeding Converters in DC Microgrids with Average Voltage Regulation and Current Sharing

   https://doi.org/10.1109/ISGTAsia49270.2021.9715610  

   Mohiuddin, Sheik M.; Qi, Junjian (December 2021, 2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia))

   This paper proposes a unified distributed secondary control for the grid-forming (GFM) and grid-feeding (GFE) converters in DC microgrids. An optimization problem is formulated for the secondary control and the objective function considers regulating the global average of the GFM and GFE converter output voltages and proportional current sharing among all GFM and GFE converters. A unified distributed control is then designed to generate voltage and current references respectively for GFM and GFE converters based on the formulated optimization problem. The dynamic model of the DC microgrid under the proposed control is also developed, and steady-state analysis is performed to show that the proposed distributed control can achieve the control objectives in steady state. The performance of the proposed control is validated through real-time simulations in OPAL-RT on an 8-DG DC microgrid system. 

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