Article
Computer Science, Information Systems
Anqi Pan, Bo Shen, Lei Wang
Summary: This article proposes a collaborative resource allocation approach for solving high-dimensional multiobjective problems. By readjusting and cooperating the resource allocation strategies for decision and objective spaces, the directional convergence is strengthened and the diversity of target regions is preserved. Experimental results demonstrate the effectiveness and rationality of the proposed approach.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Ali Thamallah, Anis Sakly, Faouzi M'Sahli
Summary: This article focuses on tracking and stabilizing issues of discrete switched systems, presenting a new Dynamic matrix control method based on multi-objective optimization and truncated impulse response model to solve general step-tracking problems and stability issues, providing a smooth multi-objective control law even in the presence of problems.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2021)
Article
Automation & Control Systems
Yuanping Su, Lihong Xu
Summary: The setpoint of the greenhouse climate has a significant impact on energy saving performance. This study proposes a decision support strategy to generate the setpoint for greenhouse climate control using online multi-objective optimization. An adaptive hybrid control method based on a greenhouse climate model is also proposed. The simulation results show that this method achieves good control performance and economic efficiency.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: Reinitialization approach involves prediction and variation operators for dynamic optimization. This study examines the impact of prediction accuracy and change severity on the optimal variation strength, and introduces an adaptive variation operator. Descriptive simulations were conducted to explore the method's learning capability and sensitivity to changes.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics, Interdisciplinary Applications
Wen Zhong, Jian Xiong, Anping Lin, Lining Xing, Feilong Chen, Yingwu Chen
Summary: The study introduces a flexible ensemble framework ASES that enhances the performance of solving multi-objective optimization problems by embedding different MOEAs. By recording large-scale nondominated solutions in a big archive and developing an efficient strategy to update the archive, the efficiency of the algorithm is improved.
Article
Automation & Control Systems
Saeed Fallah Ramezani, Ehsan Vafa, Mohammad Fakhroleslam, Mohammad Shahrokhi
Summary: In this study, the micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling was investigated using simulation. The adaptive generalized predictive control (AGPC) strategy was adopted to control the greenhouse's temperature, relative humidity, and CO2 concentration. The simulation results showed that AGPC efficiently handled variables interaction and could maintain set-points despite limitations and high outside temperature.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Chemistry, Analytical
Mingyue Zhou, Xingang Guo
Summary: In this paper, a robust adaptive target power allocation strategy is proposed for cognitive nonorthogonal multiple access (NOMA) networks, which allows single-station communication to achieve energy efficiency or high throughput by introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor. Different cognitive users can choose different communication targets, and different quality of service (QoS) can be selected by the same cognitive user at different times. With imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case and solved using the dual decomposition method. Simulation results demonstrate the strategy's good adaptive selectivity and robustness.
Article
Environmental Sciences
Angelo Carlino, Massimo Tavoni, Andrea Castelletti
Summary: Research shows that integrating multi-objective optimization and feedback control into the Dynamic Integrated Climate Economy model can design self-adaptive climate policies that balance welfare maximization with compliance to the Paris Agreement. These policies allow for adjustments based on accumulating information about the socio-climatic system, resulting in reduced warming above 2 degrees C and the probability of overshooting.
Article
Computer Science, Artificial Intelligence
Marko Durasevic, Francisco Javier Gil-Gala, Domagoj Jakobovic, Carlos A. Coello Coello
Summary: Dispatching rules (DRs) are popular methods for solving dynamic scheduling problems, but they perform poorly for multi-objective (MO) problems. Recent research has focused on using genetic programming (GP) to automatically design DRs for MO problems. However, evolving new DRs for each MO problem can be computationally expensive. To address this, we propose a methodology to combine existing DRs for optimizing individual criteria into ensembles suitable for optimizing multiple criteria simultaneously. The method outperforms standard MO algorithms in terms of performance and can be applied to problems with a smaller number of criteria.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Siwakorn Anosri, Natee Panagant, Sujin Bureerat, Nantiwat Pholdee
Summary: A general approach based on the most probable point (MPP) method is developed for solving reliability truss optimisation, with the use of double loop optimisation to achieve consistent and accurate results. Newly developed algorithms show to outperform several state-of-the-art algorithms in efficiency and accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Agriculture, Multidisciplinary
Bernardo Morcego, Wenjie Yin, Sjoerd Boersma, Eldert van Henten, Vicenc Puig, Congcong Sun
Summary: This paper aims to comprehensively analyze the connections, differences, advantages, and disadvantages between Model Predictive Control (MPC) and Reinforcement Learning (RL) in greenhouse climate control, and provide valuable insights and decision-making basis.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Industrial
Dezhi Zhang, Yarui Zhang, Shuanglin Li, Shuangyan Li, Wanru Chen
Summary: This paper investigates a bi-objective robust optimization model on relief collaborative distribution among three echelons of authorities to address the challenge of developing an effective emergency collaborative distribution system. The optimal location of relief supply facilities and the relief distribution schemes are determined by the model, which considers uncertain demand and travel time. The results show that incorporating secondary disaster scenarios reduces total travel time and cost, and the centralised decision scheme is more effective in utilizing emergency resources and preventing situations from worsening.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Energy & Fuels
Ivan Cvok, Igor Ratkovic, Josko Deur
Summary: The study presents a genetic algorithm-based control input allocation method for electric vehicle heating, ventilation, and air-conditioning systems, which efficiently reduces power consumption and enhances thermal comfort. By optimizing the control of HVAC and IRP systems, decoupling is achieved, leading to a significant reduction in power consumption while maintaining comfort levels.
Article
Mathematics, Interdisciplinary Applications
Ritu, Savin Treanta, Divya Agarwal, Geeta Sachdev
Summary: The aim of this study is to investigate multi-dimensional vector variational problems considering data uncertainty in each of the objective functional and constraints. We establish the robust necessary and sufficient efficiency conditions such that any robust feasible solution could be the robust weakly efficient solution for the problems under consideration. Emphatically, we present robust efficiency conditions for multi-dimensional scalar, vector, and vector fractional variational problems by using the notion of a convex functional.
FRACTAL AND FRACTIONAL
(2023)
Article
Environmental Sciences
D. M. McKay Fletcher, S. A. Ruiz, T. Dias, D. R. Chadwick, D. L. Jones, T. Roose
Summary: Synchronizing fertilizer timings with both crop N demand and local weather patterns can significantly enhance nitrogen use efficiency (NUE) in cropping systems. The optimal timing of nitrogen application varies with rainfall patterns, and the mobility of nitrogen in soil also affects plant nitrogen uptake.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Agricultural Engineering
Yuanping Su, Lihong Xu, Erik D. Goodman
Summary: Energy conservation is increasingly important in greenhouse production. Dynamically determined greenhouse climate setpoints can improve crop yield and reduce total energy consumption. A multi-layer hierarchical optimisation framework is proposed to deal with long-term weather uncertainty.
BIOSYSTEMS ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik D. Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti
Summary: This study proposes an evolutionary algorithm for searching neural architectures, which fills a set of architectures through genetic operations to approximate the entire Pareto frontier, improves computational efficiency, and reinforces shared patterns among past successful architectures through Bayesian model learning. The method achieves competitive performance in image classification tasks, while considering multiple objectives.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Zhichao Lu, Gautam Sreekumar, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti
Summary: NAT method efficiently generates task-specific models competitive under multiple conflicting objectives by learning task-specific supernets and integrating online transfer learning and many-objective evolutionary search. It significantly improves performance in various image classification tasks, particularly on small-scale fine-grained datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Automation & Control Systems
Chaoda Peng, Hai-Lin Liu, Erik D. Goodman
Summary: This article introduces a set of CMOPs with deceptive constraints and proposes a cooperative framework that effectively solves this problem. The framework consists of two phases, one for exploring feasible regions and the other for exploring the entire space, with the ability to switch phases based on information found during the evolutionary process.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Chaoda Peng, Hai-Lin Liu, Erik D. Goodman, Kay Chen Tan
Summary: This paper investigates the issue of updating the reference point in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A two-phase framework is proposed to locate the reference point and enhance algorithm performance, along with a set of benchmark problems to evaluate its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Yuanping Su, Lihong Xu, Erik D. Goodman
Summary: This paper proposes a hybrid surrogate-based-constrained optimization method to handle computationally expensive objective functions and constraints. It introduces a new constraint-handling method to transform the constrained optimization problem into an unconstrained problem, achieving better optimization performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Shuwei Zhu, Lihong Xu, Erik D. Goodman, Zhichao Lu
Summary: The article proposes a new multiobjective evolutionary algorithm based on the generalization of Pareto optimality, which uses the (M-1)-GPD framework to promote both convergence and diversity. Research shows that this algorithm is competitive on various benchmark problems and outperforms other methods on three real-world problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb, Erik D. Goodman
Summary: This article presents an approach that uses machine learning to learn the relationships between top solutions in optimization problems, helping offspring solutions progress. The method involves balancing tradeoffs between convergence and diversity, using the Random Forest method, and changing the application of machine learning models.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Yuanping Su, Lihong Xu
Summary: The setpoint of the greenhouse climate has a significant impact on energy saving performance. This study proposes a decision support strategy to generate the setpoint for greenhouse climate control using online multi-objective optimization. An adaptive hybrid control method based on a greenhouse climate model is also proposed. The simulation results show that this method achieves good control performance and economic efficiency.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Shuwei Zhu, Lihong Xu, Erik D. Goodman
Summary: Evolutionary multiobjective clustering algorithms can outperform single-object clustering algorithms when the number of clusters is not predetermined, but face challenges in computational burden. The proposed hierarchical, topology-based cluster representation simplifies the search procedure, leading to improved clustering performance and computing efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Multidisciplinary
Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman, Ronald Averill
Summary: This article introduces a multi-objective evolutionary algorithm framework that combines problem-specific knowledge and online innovization approaches to solve real-world large-scale multi-objective problems. The framework utilizes the knowledge of experienced users and the inter-variable relationships in good solutions to improve candidate solutions through repair operators for faster finding of good solutions.
ENGINEERING OPTIMIZATION
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ritam Guha, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb
Summary: Automated machine learning (AutoML) greatly simplifies architecture engineering by building machine-learning algorithms using basic primitives. AutoML-Zero expands on this concept by exploring novel architectures beyond human knowledge without utilizing feature or architectural engineering. However, it currently lacks a mechanism to satisfy real-world application constraints. We propose MOAZ, a multi-objective variant of AutoML-Zero, which trades off accuracy with computational complexity, distributes solutions on a Pareto front, and efficiently explores the search space.
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
(2023)
Article
Computer Science, Information Systems
Yuanping Su, Lihong Xu
Summary: Greenhouse climate setpoint optimization is challenging due to the great uncertainty of weather. This study proposes an online receding horizon multi-objective optimization method and addresses the challenge with surrogate/interpolation methods. The proposed method shows advantages over the traditional Priva system based on real greenhouse data.