Ke Qu, Xiangjie Chen, Saffa Riffat · EasyChair preprint · 2018
In this review, we focus on the holistic approach technologies and strategies of energy-efficient residential building retrofitting. Besides, energy balance between both energy demand side and renewable energy supply side are discussed with the integration of different renewable energy technologies, such as advanced insulation, high efficient heat pump, solar aided energy system, combined heat and power plants, dynamic control strategies. Moreover, this review especially focused on the retrofitting problems and challenges of traditional residential buildings which is mostly built before 1960s, which accounts for 14.5% of the overall 〖CO〗_2 emission based on 2015 levels. Combined with these retrofit technologies, several reference residential building cases in Europe are analysed, indicating six main problems from pre-retrofit simulation to retrofit operation process that obstacle the retrofitting performance. To bridge the performance gaps, this review discussed the building performance simulation models and optimization algorithms. The optimization function have mathematical characters of multi-objective, multi-variability, multi-disciplinary, multi-constrained (4M) with discrete, non-linear characteristics. Applying the optimization strategies model is an essential to build a deep understanding between building performance simulation (BPS) and building performance optimization (BPO), which help us to make an optimised holistic retrofitting decisions.
Cited by 1Open access
Farzana Haque Toma · Open University of Cape Town (University of Cape Town) · 2025
Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex design problem as it has many conflicting objectives. Sunshades significantly influence a building's thermal performance, daylight quality, occupant comfort, and energy usage. However, traditional sunshade designs typically focus on a limited set of objectives, often ignoring broader considerations such as cost efficiency and outside-view obstruction. This thesis addresses that gap by implementing and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight Illuminance (UDI), cost, and outside-view obstruction. A single-room office model was used as a test bed, with parameterized sunshades simulated through Honeybee, EnergyPlus, and Radiance. Experiments were conducted in four distinct climate zones—Cape Town (moderate), Nairobi (hot), Colombo (hothumid), and Oslo (cold)—to ensure broad applicability. Both algorithms consistently outperformed traditional, manually designed sunshades in reducing thermal discomfort and energy usage while also improving UDI. Gains in cost and view preservation were more modest, primarily because minimal overhang sunshades can already be inexpensive and unobtrusive. Statistical tests indicated no systematic performance advantage of one algorithm over the other; NSGA-II tended to produce larger Pareto fronts, whereas MOCMA-ES explored a broader range of objective values. The main contribution of this research is the use of two advanced multi-objective evolutionary algorithms to optimize sunshade designs based on five key objectives, tested in four climate zones representing both the northern and southern hemispheres, as well as regions below and above the equator, demonstrating clear advantages over traditional, manually designed sunshades in achieving a balanced trade-off among competing performance criteria.
Cited by 0
Jayashree Piri, Pragyan Paramita Das, Raghunath Dey et al. · The Open Bioinformatics Journal · 2026
Introduction Infant mortality is a pivotal indicator of community health and socioeconomic conditions. Despite global advancements in healthcare and significant reductions in infant mortality rates, substantial disparities persist, particularly in underserved populations. This research aims to tackle these disparities by enhancing the predictive accuracy of public health interventions. We utilize advanced feature selection techniques to identify critical predictors of infant survival, thereby supporting the development of targeted and effective health policies and practices. Methods We introduce an enhanced Binary Multi-Objective Cheetah Optimization algorithm (BMOCO), specifically designed for feature selection in extensive medical datasets. The suggested method focuses on optimizing eight S-shaped and V-shaped transfer functions to refine the conversion of continuous position vectors into binary form, ensuring precise feature selection and robust model performance. Results The BMOCO method demonstrates superior accuracy and effectiveness in feature selection compared to traditional evolutionary optimization algorithms such as MOGA (Multi-Objective Genetic Algorithm), MOALO (Multi-Objective Ant Lion Optimizer), NSGA-II (Non-dominated Sorting Genetic Algorithm II), and MOQBHHO (Multi-Objective Quadratic Binary Harris Hawk Optimization). Applied to U.S. infant birth data, our approach achieves an average classification accuracy of 99.54%. Critical factors impacting infant mortality identified include maternal literacy, prenatal care frequency, and pre-existing maternal conditions, such as diabetes, smoking during pregnancy, body mass index, infant birth weight, and breastfeeding practices. These findings indicate that the optimized BMOCO model provides interpretable and data-driven insights that align with established clinical evidence. Discussion The results underscore the effectiveness of advanced machine learning techniques in uncovering significant health predictors. Conclusion The proposed BMOCO algorithm offers a robust and interpretable tool for health professionals to enhance predictive models, facilitating targeted interventions to reduce infant mortality rates and improve public health outcomes.
Cited by 0Open access
Shenjia Zhao, Hairui Zhang, Rui Lyu · Journal of Physics Conference Series · 2024
Abstract The proposed dynamic multi-objective evolutionary algorithm, DMOEA/D-HP, addresses temporal variations in both the Pareto Front (PF) and Pareto Set (PS) for dynamic multi-objective optimization problems (DMOPs). Utilizing a hybrid prediction approach, the algorithm adapts to the dynamic nature of the problem. The population is divided into three segments for prediction: individuals with a distance greater than a threshold in PS for central prediction, those with a distance less than a threshold in PS for differential evolutionary prediction, and the remaining individuals for cross-mutation to maintain diversity. To assess DMOEA/D-HP’s effectiveness, it is compared with three advanced algorithms in DMOP by using the DF test set. Experimental results demonstrate that DMOEA/D-HP outperforms in terms of distribution and convergence when solving DMOPs.
Cited by 0Open access
Joshbie Love A De Guzman, Guzman Alliah Cel C. Villagomez · International Journal of Research Publication and Reviews · 2024
This literature review offers a comprehensive analysis of the design and optimization of microstrip patch antennas for wireless communication systems, drawing insights from a variety of research papers.It examines diverse approaches aimed at enhancing antenna performance and functionality.Key topics explored include optimization of resonant frequency, design of antenna arrays, and bandwidth enhancement.Noteworthy optimization techniques, such as genetic algorithms and artificial neural networks, are discussed, highlighting their effectiveness in achieving superior antenna performance.Application-specific solutions tailored to areas such as military communication and RFID systems are emphasized.The review underscores the significant role played by optimization techniques in achieving desired performance metrics and identifies future research directions, including multi-objective optimization, investigation of robustness and reliability, and integration with advanced technologies.Overall, this literature review provides valuable insights into the state-of-the-art in microstrip patch antenna design and optimization, guiding future research endeavors in this dynamic and rapidly evolving field.
Cited by 3Open access
Ali Soofastaei · IntechOpen eBooks · 2025
Scheduling is critical in optimizing time and resources across industries, yet traditional scheduling methods struggle to manage the complexity and dynamic nature of modern operational environments. This chapter explores how artificial intelligence (AI) and advanced analytics transform scheduling by providing innovative solutions to age-old challenges. Through machine learning, predictive analytics, and optimization algorithms, AI-driven systems are enabling real-time decision-making and demonstrating adaptability by adjusting schedules to evolving conditions, thereby delivering higher efficiency. The chapter delves into key AI techniques such as genetic algorithms, dynamic scheduling, and multi-objective optimization, showcasing how these tools are applied in manufacturing and logistics industries. Real-world examples illustrate the tangible benefits AI offers, from reducing production downtime to improving patient care and optimizing delivery routes. This emphasis on practical value is intended to convince the audience of AI’s significant impact on scheduling. Looking forward, the chapter explores emerging trends such as autonomous scheduling, the integration of AI with the Internet of Things (IoT), and the potential of quantum computing to revolutionize scheduling further. Ultimately, this chapter demonstrates that AI and advanced analytics are solving today’s complex scheduling problems and reshaping the future of time optimization across industries.
Cited by 1Open access
Hüseyin Tarık Niş, Ahmet Yıldız · Applied Sciences · 2024
This study presents the complex transfer matrix method (CTMM) as an advanced mathematical model, providing significant advantages over the finite element method (FEM) by yielding rapid solutions for complex optimization problems. In order to design a more efficient structure of a three-stage turbomachine rotor, we integrated this method with various optimization algorithms, including genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), gravitational search algorithm (GSA), black hole (BH), particle swarm optimization (PSO), Harris hawk optimization (HHO), artificial bee colony (ABC), and non-metaheuristic pattern search (PS). Thus, the best rotor geometry can be obtained fast with minimum bearing forces and disk deflections within design limits. In the results, the efficiency of the CTMM for achieving optimized designs is demonstrated. The CTMM outperformed the FEM in both speed and applicability for complex rotordynamic problems. The CTMM was found to deliver results of comparable quality much faster than the FEM, especially with higher element quality. The use of the CTMM in the iterative optimization process is shown to be highly advantageous. Furthermore, it is noted that among the different optimization algorithms, ABC provided the best results for this multi-objective optimization problem.
Cited by 2Open access
Babak Fahimi, Osama A. Mohammed, Hamid A. Toliyat et al. · IEEE Transactions on Energy Conversion · 2015
The papers in this special section cover the advances in multi-objective design and optimization based on genetic algorithms, surrogate, polynomial regression, and particle swarm optimization techniques. Design of novel trans-rotary magnetic gear, hybrid machines for dc generation, and two degrees of freedom split-stator induction machines highlight new magnetic configurations and their performance evaluations. Multiphysics analysis of magnetothermal and magnetostructural behavior of Switched Reluctance Machine (SRM) and brushless doubly fed machines shed light on analytical and practical aspects of design in electromechanical converters. Finally, control of torque undulation, loss minimization, low speed high torque operation, and advanced digital control of adjustable speed motor drives conclude this unique collection of papers.
Cited by 11Open access
Guojun Zhang, Weidong Huang, Tong Zhou · Electrical Science & Engineering · 2024
Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.
Cited by 3Open access
Xin Wen, Qi Shen, Wenxuan Zheng et al. · International Journal of Innovative Research in Engineering & Management · 2024
This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.
Cited by 35Open access
Ahmed S. Hashim, Riries Rulaningtyas, Kalaivani Chellappan · Jurnal Kejuruteraan · 2025
Blockchain technology is a forefront innovation that drives multi-user data accessibility with patient ownership, traceability, transparency, security, and privacy protection. However, optimizing blockchain latency and its relationship with security remains underexplored in health data management. This study aims to optimize security and latency in healthcare data management through a novel Blockchain-based framework. The objectives include: (1) developing an innovative EMR management architecture that integrates key stakeholders using Blockchain and smart contracts, (2)creating a multi-objective optimization model for Hyperledger Fabric Blockchain in medical health management, and (3)implementing and evaluating advanced optimization techniques using block Size and consensusLevels as key decision variables. This study bridges the gap by proposing a novel methodology that integrates the Hyperledger Fabric framework with smart contracts to optimize security and storage in health data systems. A key contribution of this research is the development of a multi-objective optimization model employing two advanced non-dominated sorting genetic algorithms, NSGA-II and NSGA-III, to configure system parameters such as blockSize and consensusLevels. Evaluation results show that NSGA-II outperforms NSGA-III, achieving 50% higher Hyper-volume and five distinct non-dominated solutions compared to two, demonstrating its superior ability to balance conflicting objectives and provide optimal configurations. The proposed framework tackles critical challenges in health data interoperability, privacy, and security while ensuring robust, patient-centric data management. By enabling multi-user access, enhanced security, and optimized storage, this work advances Blockchain’s role in healthcare and positions the industry for the Digital Health era. Future work will expand objectives and enable real-time deployment.
Cited by 1Open access
Yesong Wang, Zhan-Li Sun, Jisheng Liu et al. · PLoS ONE · 2024
To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.
Cited by 6Open access
Vamsi Krishna Reddy, Aala Kalananda, Venkata Lakshmi Narayana, Komanapalli · RePEc: Research Papers in Economics
Optimization through meta-heuristics in electric vehicular (EV) transport has emerged as the key to improve the existing technologies and pave way for their mass deployment and revolutionize the current transport system while lowering greenhouse emissions. Range and cost have been the main aspects that continue to hinder the development of EVs. This paper provides a comprehensive outlook of the five major areas of optimization (over the last two decades) in EVs i.e., design optimization, energy management, optimal control, optimized charging/discharging and routing with both single and multi-objective methods examined and analyzed. The mathematical modelling, formulation of objective functions and constraints are studied followed by an in-depth survey of the state-of-the-art publications in each category. Furthermore, a classification of the various analytical, conventional and nature-inspired optimization algorithms (swarm-intelligent, evolutionary and modern meta-heuristics) based on their popularity is made with an analysis of their merits and demerits followed by a study of the various constraint handling techniques. Additionally, a literature survey of the advanced and improved variants of these meta-heuristics is also made providing a systematic reference for EV optimization with intelligent algorithms. Finally, a classification of the various simulatory platforms and driving cycles is provided to help the upcoming researchers gain insightfulness and expertise on the ongoing trends in the EV optimization domain.
Cited by 0
Hicks, Andre · DSpace@MIT (Massachusetts Institute of Technology) · 2022
The emergence of advanced engineering technologies creates the opportunity to improve existing manual and silo-ed workflows within integrated energy companies. The processes used for drilling engineering have not evolved at the pace of cutting-edge technology advancements over the last 20 years. The most significant shifts in classical well development are standardized design methods, advanced disciplinary analysis, improved knowledge transfer systems, excel-based workflows, and structured employee training. \n \n169As the Industrial Revolution 4.0 progresses, technologies in Model-Based Systems Engineering are emerging to enhance existing well design processes, yet the step change is insufficient to close the technology gap. This research contributes to existing drilling engineering and well design advancements by developing a system optimization architecture for the well design process. A random search algorithm coupled with a stochastic optimization methodology for multi-objective optimization emerges through the relationships defined within a system Design Structure Matrix (DSM). The optimization method includes the evaluation of the algorithms’ computational efficiency, design diversity, and convergence. The development of a numerical solution for well design, will provide the framework necessary to implement advanced analysis of well design that can accurately predict the quality of engineering decision-making.
Cited by 0Open access
Chiweta E. Abunike, Milad Dowlatshahi, Ali Far et al. · Results in Engineering · 2025
This study introduces a novel multilevel design optimization approach for enhancing the performance of brushless flux-switching wound-field machines (FSWFMs) in electric vehicles (EVs) and industrial drives. The proposed methodology targets key performance metrics namely, high torque, efficiency, power factor, and low torque ripple through a structured sensitivity analysis categorized into non-sensitive, mild-sensitive, and strong-sensitive levels. Using the Response Surface Method (RSM), Min-Max Search, and Multi-Objective Genetic Algorithms (MOGA), the Response Surface Multi-Level Optimization (RSMLO) method effectively harmonizes these competing objectives. The optimization process resulted in an 11% increase in average torque and a 69.06% reduction in torque ripple, demonstrating significant performance gains. These results underscore the potential of the RSMLO method as a robust tool for the advanced design of electric machines, offering substantial improvements in both performance and efficiency, and positioning it as a critical framework for future EV and industrial drive applications.
Cited by 5Open access
Xin Guo, Yue Zhang, Jian Zhang et al. · Journal of Building Physics · 2025
This study presents an innovative multi-objective optimization framework for sustainable building design, addressing the intricate balance between building morphology and building performance in the early design phase. By integrating four critical attributes—energy consumption, cost, functionality, and thermal comfort—into a cohesive model, this framework offers a comprehensive evaluation approach. The proposed methodology combines the Penalty Function (PF) method, Generative Adversarial Networks (GAN), and the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to navigate the complex trade-offs and interconnections among these objectives. Through a series of numerical experiments and multi-scenario case studies, the framework demonstrates its ability to achieve high-quality Pareto-optimal solutions, effectively balancing multiple objectives. The results confirm the framework’s effectiveness and reliability, highlighting its potential for practical application in sustainable building design. This research contributes to the field by proposing a more holistic optimization model, enhancing the optimization process through advanced algorithms, and providing a robust tool for sustainable building design.
Cited by 0
Shu–Yu Kuo, Kun-Lin Lee, Jyun–Yi Shen et al. · 2025
Quantum finance has emerged as a key research area as interest grows in leveraging quantum computing for solving complex optimization problems in financial technology. Quantum annealing (QA), which leverages quantum tunneling effects, presents a promising method for tackling portfolio optimization challenges that primarily involve balancing risk and return. Traditional single-solution optimization approaches often fail to accommodate diverse investor preferences regarding risk tolerance and expected returns. To bridge this gap, we propose a novel bi-objective weighted portfolio optimization framework based on the trend ratio (TR) model. Our approach integrates D-Wave's advanced constrained quadratic model (CQM) hybrid solver with QA and employs scalarization-based non-dominated analysis, enabling multiple runs with different weights between risk and return to effectively achieve bi-objective optimization. This flexible strategy generates a diverse set of high-quality solutions tailored to different investor profiles. In our experiments, we investigate the interaction between two well-known international stock indices, DJI and Nikkei, to gain insights into cross-market dynamics and enhance portfolio diversification. Experimental results show that the proposed approach consistently demonstrates strong potential compared to conventional multi-objective optimization algorithms, delivering solutions with improved quality and computational efficiency. These findings highlight the potential of quantum computing in advancing financial decision-making and portfolio optimization.
Cited by 0
Zhen Zeng, Le Gao, Zhiyong Hong et al. · 2025
This paper investigates the energy-aware scheduling of a two-stage flexible flow shop considering the inconsistency of process differences of jobs, with the aim of reducing energy consumption using group technology. A multi-objective optimization problem is addressed through the proposal of an enhanced multi-objective scatter search algorithm. The experimental results demonstrate that group technology not only reduces energy consumption and make span but also effectively balances machine load through a dynamic split strategy. Comparison with existing algorithms reveals the superior performance of the improved multi-objective scatter search algorithm. As consumer demands evolve, traditional manufacturing systems must undergo transformation and upgrades to meet advanced technological requirements while enhancing production efficiency and cost reduction. This study provides a valuable reference for enterprises operating under the multi-variety and small batch manufacturing mode to minimize energy consumption costs and improve production efficiency. Future work will explore group strategies considering job delivery time, optimize energy consumption costs under time-of-use electricity tariffs, and address algorithm speed in solving large-scale problems.
Cited by 0
Amos H.C. Ng, Florian Siegmund, Kalyanmoy Deb · Journal of Systems and Information Technology · 2018
Purpose Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms. Design/methodology/approach Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations. Findings This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process. Originality/value Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.
Cited by 5
Nezif Tamson, Hüseyin Hacı, Arif Sarı · Hydrology research · 2024
ABSTRACT This study explores the optimization of dynamic voltage restorers (DVRs) within hydro turbine systems using the Bee Optimization Algorithm (BOA), focusing on enhancing power system stability and quality through advanced control strategies. Emphasizing total harmonic distortion (THD) minimization, the research addresses power quality challenges prevalent in hydroelectric power generation. Detailed simulations demonstrate how BOA effectively reduces THD, optimizing DVR performance in response to grid disturbances typical in renewable energy integrations. Findings validate the BOA's efficacy in improving voltage stability and underscore the potential of bio-inspired algorithms for smart grid applications in hydro settings. This approach not only supports the reliability of hydroelectric power systems but also opens new avenues for employing multi-objective optimization techniques to advance DVR functionality, contributing to the sustainable management of energy infrastructures.
Cited by 0Open access