Article
Computer Science, Artificial Intelligence
Ehsan Ardjmand, Manjeet Singh, Heman Shakeri, Ali Tavasoli, William A. Young
Summary: This study revisits the order batching problem by introducing a new overlap objective to measure the risk of infection spread, and proposes a multi-objective optimization model and three multi-objective metaheuristics. Through experiments, it is found that picking capacity can have a determining impact on reducing the risk of infection spread by minimizing picking overlap.
APPLIED SOFT COMPUTING
(2021)
Article
Green & Sustainable Science & Technology
Peng Sun, Teng Yun, Zhe Chen
Summary: The close interaction between biomass waste disposal energy supply and multi-energy systems is proposed in a MEMG framework, with a focus on flexible regulation and optimization to minimize costs and maximize waste disposal.
Article
Energy & Fuels
Musik Park, Zhiyuan Wang, Lanyu Li, Xiaonan Wang
Summary: With increasing concerns over carbon dioxide emissions, the concept of Zero Energy Building (ZEB) and Electric Vehicles (EVs) have emerged to address environmental issues. This paper develops a new framework to find the optimal energy system design that meets EV charging demand and ZEB requirements, using machine learning models to predict charging demand and Genetic Algorithm and PROBID method to optimize costs and self-sufficiency ratio. The study finds that EV charging demand significantly affects energy system design, especially in small-size buildings.
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Linfei Yin, Wenyu Ding
Summary: This study aims at the operation control of doubly-fed induction generator-wind energy systems. The study introduces multi-dimensional information feedback and fractional-order theory, and designs a high-dimensional multi-fractional-order controller. Additionally, a high-dimensional multi-fractional-order optimization method is proposed for the tuning optimization of controller parameters.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Rodrigo Olivares, Francisco Munoz, Fabian Riquelme
Summary: A new optimization model was proposed to address both perspectives under conflict through the LT-model using a binary multi-objective approach, and swarm intelligence methods were implemented to solve the proposal on real networks. The results are promising, suggesting that the new multi-objective solution can be effectively solved even in harder instances.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Energy & Fuels
Sudlop Ratanakuakangwan, Hiroshi Morita
Summary: This study proposes a combination of multi-objective optimization and efficiency measurement for determining an efficient energy mix in energy planning. It considers various dimensions of energy planning and associated uncertainties. The proposed model includes multiple objective functions related to energy need, cost, environmental impact, security, social impact, and social benefit. The slacks-based measure methodology is applied to identify the best energy mix. The results show significant improvements in reducing emissions and dependence on certain power plant types, increasing employment and the proportion of electricity generated from renewable sources, with slight tradeoffs in costs. The quantitative results from the model can assist policymakers in efficiently determining an energy policy that optimizes various aspects under given constraints and scenarios of uncertainty.
Article
Mathematics
Jeewon Park, Oladayo S. Ajani, Rammohan Mallipeddi
Summary: Recently, optimization-based energy disaggregation algorithms have gained significance as they require less data for training compared to pattern-based algorithms. However, the performance of these algorithms depends on the problem formulation, including objective functions and constraints. In this study, the energy disaggregation problem is formulated as a constrained multi-objective problem and solved using a constrained multi-objective evolutionary algorithm. The proposed formulation is compared to three high-performing formulations in the literature, and the results show improvements in both appliance-level and overall energy disaggregation.
Article
Thermodynamics
Zhiqiang Liu, Yanping Cui, Jiaqiang Wang, Chang Yue, Yawovi Souley Agbodjan, Yu Yang
Summary: This study explores an optimization model for properly sizing a multi-energy complementary integrated energy system (MCIES) considering uncertainties and achieving the best economic, environmental, and thermal comfort benefits. The non-dominated sorting genetic algorithm-II (NSGA-II) combined with TOPSIS and Shannon entropy method is used for optimization. Case studies demonstrate the effectiveness of the proposed approach.
Article
Green & Sustainable Science & Technology
Leyi Yao, Zeyuan Liu, Weiguang Chang, Qiang Yang
Summary: This paper proposes a multi-level optimization model for real-time optimal operation of integrated energy systems (IES) with renewable energy systems (RES), energy storage systems (ESS) and carbon capture systems (CCS). Uncertainty is quantified using the Conditional Value at Risk (CVaR) theory and reduced through a model predictive control (MPC)-based method. A multi-objective optimization model is adopted to minimize economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling. Results show that the proposed solution provides a trade-off between economical and environmental performance.
Article
Thermodynamics
Yiyang Qiao, Fan Hu, Wen Xiong, Zihao Guo, Xiaoguang Zhou, Yajun Li
Summary: This paper proposed a multi-objective optimization method based on energy hub, focusing on the installation configuration of gas turbines. By establishing a model and using algorithms, it demonstrated the importance of installation configuration in saving costs and reducing energy consumption and emissions.
Article
Construction & Building Technology
Riccardo Albertin, Alessandro Prada, Andrea Gasparella
Summary: The energy design of buildings requires finding trade-offs between conflicting goals, and optimizing actual building designs is still a challenge. A new efficient multi-objective algorithm based on a probabilistic approach is proposed to reduce computational time and improve solution quality. The algorithm was tested on different cases and outperformed other algorithms in terms of solution accuracy.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Martina Capone, Elisa Guelpa, Vittorio Verda
Summary: Effective management strategies are crucial for achieving reductions in energy consumption and carbon dioxide emissions in district energy applications. A global optimization approach is proposed in this paper, which combines the optimization of production side with demand-side management to improve the operation of smart energy systems. By using a bi-level optimization structure, significant reductions in emissions can be achieved at the expense of a modest increase in operating cost.
Article
Computer Science, Artificial Intelligence
Weimin Huang, Wei Zhang
Summary: An adaptive MOPSO with multi-strategy algorithm, ecemAMOPSO, is proposed to improve the performance in solving multi-objective optimization problems. The algorithm shows competitive results in experimental studies on benchmark suits.
APPLIED SOFT COMPUTING
(2021)
Article
Green & Sustainable Science & Technology
Chengzhou Li, Ligang Wang, Yumeng Zhang, Hangyu Yu, Zhuo Wang, Liang Li, Ningling Wang, Zhiping Yang, Francois Marechal, Yongping Yang
Summary: This study proposes a multi-objective optimization methodology for planning distributed energy systems considering process synergy and thermal integration. The system design and dispatch strategy are optimized to achieve economic benefit, reduce carbon emission, and decrease fossil fuel consumption. A new multi-energy complementary distributed energy system is developed, which includes solar energy utilization and hybrid energy storage technologies. The results show significant cost reduction and environmental benefits compared to traditional energy systems. The optimal design of the system provides a reference for decision-making and flexible operation.
JOURNAL OF CLEANER PRODUCTION
(2023)
