Article
Computer Science, Artificial Intelligence
Emrullah Sonuc, Ender Ozcan
Summary: Metaheuristics, which provide high-level guidelines for heuristic optimization, have been successfully applied to complex problems. However, their performance varies depending on the initial settings and problem characteristics. Therefore, there is a growing interest in designing adaptive search methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Pedro Simao, Miguel Vieira, Telmo Pinto, Tania Pinto-Varela
Summary: This paper introduces a method for incorporating renewable energy into industrial production, optimizing energy utilization through integrated design and scheduling. The results show that using solar energy as thermal energy storage can minimize energy consumption and increase operational profit.
Article
Environmental Sciences
Wenyan Wu, Yuerong Zhou, Michael Leonard
Summary: The study compares different methods for incorporating uncertainty into multiobjective reservoir operation policy optimization, highlighting their advantages and limitations. Each method has its own pros and cons, and careful consideration is required when selecting the most suitable approach based on research needs.
ENVIRONMENTAL RESEARCH COMMUNICATIONS
(2022)
Article
Biotechnology & Applied Microbiology
Jan Sadoch, Monika Pyc, Anna Urbanowicz, Adam Iglewski, Radoslaw Pilarski
Summary: By optimizing the induction medium, protein expression in BY-2 tobacco cells can be significantly increased. Use of a reliable XVE/GFP model, conducting parallel experiments on microscale in 96-well plates, and dedicated Gene Game evolutionary optimization software allows for an effective search for optimal media combinations.
BIOTECHNOLOGY AND BIOENGINEERING
(2021)
Article
Computer Science, Information Systems
Pawel B. Myszkowski, Maciej Laszczyk
Summary: The paper introduces a novel many-objective evolutionary method that aims to increase diversity and spread in the Pareto Front approximation. Experimental results show that guiding the evolution process towards less explored parts of a space can lead to increased diversity but may also increase convergence. The introduction of a novel selection operator is shown to circumvent the issue of existing diversity mechanisms in combinatorial spaces.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Fakhar Uddin, Naveed Riaz, Abdul Manan, Imran Mahmood, Oh-Young Song, Arif Jamal Malik, Aaqif Afzaal Abbasi
Summary: The travelling salesman problem (TSP) is extensively studied and has practical applications. This article proposes a simple and improved heuristic algorithm called 2-Opt++ which solves symmetric TSP problems using enhanced local search technique to generate better results. Moreover, the algorithm can also be applied to the Vehicle Routing Problem (VRP).
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Pengcheng Jiang, Yu Xue, Ferrante Neri
Summary: Dropout is an effective method for training deep neural networks by deactivating some neurons to mitigate overfitting. This paper proposes a novel approach to guide the dropout rate using an evolutionary algorithm, allowing for more flexibility in training. Experimental results demonstrate that this method consistently outperforms other dropout methods, including state-of-the-art techniques.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Edgar Galvan, Fergal Stapleton
Summary: This study makes progress in neuroevolution for vehicle trajectory prediction by adopting rich artificial neural networks and two evolutionary multi-objective optimization algorithms. The underlying mechanisms and response to objective scaling of each algorithm are revealed. Additionally, certain objectives are found to be beneficial while others are detrimental to finding valid models.
APPLIED SOFT COMPUTING
(2023)
Article
Construction & Building Technology
Egon Vettorazzi, Antonio Figueiredo, Filipe Rebelo, Romeu Vicente, Gabriel Alves Feiertag
Summary: As buildings are responsible for a fifth of greenhouse gas emissions, improving their energy efficiency is seen as a crucial opportunity for climate neutrality. This study examines the impact of optimizing the geometrical characteristics of building envelopes on reducing energy consumption in different climatic scenarios. The findings show that optimizing window areas and shading solutions, along with the Passive House concept, can significantly reduce energy demand and improve thermal comfort.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Management
Mounir Hafsa, Pamela Wattebled, Julie Jacques, Laetitia Jourdan
Summary: In this paper, a method is proposed to solve the real-world timetabling problem at Mandarine Academy. The motivation is to provide an automated professional course scheduling tool to replace the inefficient manual process. A mathematical model with 18 constraints and five competing objectives is presented, and several multi-objective evolutionary algorithms are tested. Custom genetic operators are proposed and compared to conventional operators, and elite configurations are obtained through a tuning phase. Experiments with real-world data and various metrics are conducted to evaluate the algorithms' performance.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Thermodynamics
Shanshuo Xing, Jili Zhang
Summary: The authors propose a hybrid optimisation method for a chiller-pump system in parallel-arranged systems with non-identical chillers and pumps. The method achieves overall minimum energy consumption through trade-off between chillers and pumps.
APPLIED THERMAL ENGINEERING
(2022)
Article
Engineering, Industrial
Wenbo Wu, Zhengdong Huang, Jiani Zeng, Kuan Fan
Summary: This paper proposes a deep reinforcement learning approach to solve the assembly sequence planning (ASP) problem, aiming to improve response speed by leveraging the reusability and expandability of past decision-making experiences. Through steps like instance generation algorithm, mask algorithm, and Monte Carlo sampling method, the assembly cost is minimized effectively under precedence constraints. It is demonstrated that the method accurately and efficiently solves the ASP problem in an environment with dynamic resource changes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Water Resources
Ravindra Kumar Singh, Satish Kumar, Srinivas Pasupuleti, Vasanta Govind Kumar Villuri, Ankit Agarwal
Summary: Evolutionary algorithms (EAs) have proven effective in solving controlled, nonlinear multimodal, non-convex problems that are difficult for deterministic approaches. This study used three EAs, genetic algorithms (GAs), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA), to calibrate and validate the storm water management model (SWMM) in the upper Damodar River basin. The results showed that GA performed superiorly, with NSE and PBIAS values ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively. The RSR value was approximately 0, indicating the exceptional performance of the model. The use of the hydrodynamic model with EA has potential for analyzing vulnerability in river watersheds.
JOURNAL OF WATER AND CLIMATE CHANGE
(2023)
Article
Automation & Control Systems
Pengcheng Fang, Jianjun Yang, Qingmiao Liao, Ray Y. Zhong, Yuchen Jiang
Summary: The study introduced a worker allocation problem in an aircraft final assembly line, which was addressed using integer programming formulation and an improved genetic algorithm, resulting in significant reduction of production time in a real-world case.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Edric Matwiejew, Jingbo B. Wang
Summary: This article introduces a Python package called QuOp_MPI, which is designed for parallel simulation of quantum variational algorithms. The package utilizes an object-oriented approach and MPI-parallelized techniques such as sparse matrix exponentiation, fast Fourier transform, and parallel gradient evaluation to achieve efficient simulation of fundamental unitary dynamics on massively parallel systems. The article also explores the application of QuOp_MPI in simulating quantum algorithms for solving combinatorial optimization problems.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Lixin Tang, Yun Dong, Jiyin Liu
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2015)
Article
Engineering, Industrial
Lixin Tang, Defeng Sun, Jiyin Liu
Article
Operations Research & Management Science
Lixin Tang, Feng Li, Jiyin Liu
NAVAL RESEARCH LOGISTICS
(2015)
Article
Management
Lixin Tang, Ying Meng, Zhi-Long Chen, Jiyin Liu
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2016)
Article
Computer Science, Artificial Intelligence
Lixin Tang, Yue Zhao, Jiyin Liu
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2014)
Article
Engineering, Industrial
Lixin Tang, Xie Xie, Jiyin Liu
Article
Engineering, Industrial
Lixin Tang, Wei Jiang, Jiyin Liu, Yun Dong
Article
Automation & Control Systems
Chang Liu, Lixin Tang, Jiyin Liu
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2020)
Article
Automation & Control Systems
Guodong Zhao, Jiyin Liu, Lixin Tang, Ren Zhao, Yun Dong
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2020)
Article
Automation & Control Systems
Lixin Tang, Xiangman Song, Jiyin Liu, Chang Liu
Summary: The Estimation of Distribution Algorithm (EDA) proposed in this article utilizes Kalman filtering and a learning strategy to address issues related to nonlinearity, variable coupling, and large-scale optimization problems. Computational experiments demonstrate the effectiveness of the algorithm. In practical applications, it has the potential to optimize process control parameters for continuous production processes like blast furnaces.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Automation & Control Systems
Zuocheng Li, Lixin Tang, Jiyin Liu
Summary: This article proposes a memetic algorithm based on probability learning to solve the multidimensional knapsack problem (MKP), highlighting the problem-dependent heuristics and a novel framework. Experimental results demonstrate the effectiveness and practical values of the proposed method for MKP.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Pol Arias-Melia, Jiyin Liu, Rupal Mandania
Summary: This paper examines the problem of vehicle sharing and task allocation, proposing an integer programming model and a heuristic algorithm. Results show that sharing vehicles can save on vehicle usage and reduce carbon emissions.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yizi Zhou, Anne Liret, Jiyin Liu, Emmanuel Ferreyra, Rupal Rana, Mathias Kern
ARTIFICIAL INTELLIGENCE XXXIV, AI 2017
(2017)
Article
Automation & Control Systems
Shuo Liu, Wen-Hua Chen, Jiyin Liu
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
(2016)