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IEEE Access, 2021 A well-designed demand response (DR) program is essential in smart home to optimize energy usage according to user preferences. In this study, we proposed a multiobjective reinforcement learning (MORL) algorithm to design a DR program.
Song-Jen Chen, Wei-Yu Chiu, Wei-Jen Liu
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IEEE Access, 2022 Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of
Akash Singh +3 more
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IEEE Access, 2023 In this paper, a quality diversity optimization method (QDOM) based on an adaptive bound-searching algorithm and diversity-selecting immune algorithm is proposed for solving bilinear matrix inequality (BMI) problems in control system design. By using the
Shiuan-Yeh Chen +2 more
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Multi-objective congestion control
Proceedings of the Seventeenth European Conference on Computer Systems, 2022 Decades of research on Internet congestion control (CC) has produced a plethora of algorithms that optimize for different performance objectives. Applications face the challenge of choosing the most suitable algorithm based on their needs, and it takes tremendous efforts and expertise to customize CC algorithms when new demands emerge.
Ma, Yiqing +6 more
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Multi objective project portfolio selection [PDF]
Journal of Project Management, 2019 The Project Portfolio Selection is a complex process that involves many factors and considerations since the project is proposed until the project portfolio is finally selected.
Kamal Baqeri +2 more
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Multi-Objective Evolutionary Federated Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices.
Zhu, Hangyu, Jin, Yaochu
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IET Generation, Transmission & Distribution, 2023 With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted.
Mehdi Davoudi +2 more
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IET Generation, Transmission & Distribution, 2023 An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF).
Aamir Ali +5 more
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Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization
Complex & Intelligent Systems, 2022 Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation.
Ke-Jing Du +3 more
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Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics
Advanced Science, 2022 Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited.
Peter Sagmeister +7 more
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