Qamariddinov Shohruh Akmal o'g'li · Zenodo (CERN European Organization for Nuclear Research) · 2025
This study examines techniques to enhance the efciency of mechatronic modules in wheat tempering throughvirtual prototyping (digital twin), advanced moisture sensors, and multi-objective optimization. The proposed frameworkleverages genetic algorithms and artifcial neural networks to improve moisture uniformity by ≥ 20% and reduce waterenergy consumption by ≥ 10%. Pilot-scale experiments validated the model with a determination coefcient of R² ≥ 0.95,demonstrating robust real-time control performance.
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Xinjian Lv, Zongyao Yan · 2022
The agent routing process considering weather conditions is a multi-objective optimization problem constrainedly, which various optimization criteria and constraints should be considered. The quality of the given routes depends not only on the quality of the data, but also on the modelling of the optimization problem and the algorithm chosen to solve it. Because of universality of mathematical methods and the resulting decision-making challenges, the aim of this paper is to comprehensively compare the existing most advanced methods through literature review and elaborate different modelling methods, optimization algorithms and their application in agent ship route planning.
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Bahman Naderi, H. Sadeghi · SHILAP Revista de lepidopterología · 2012
This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective simulated annealing algorithm (MOSA). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOSA provides sound performance comparing with other algorithms.
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Rongkai Zhang, Lili Zhao · 2026
Traditional LED lighting systems lack comprehensive circadian rhythm control capabilities, resulting in insufficient consideration of biological impacts and sub-optimal user wellbeing. The complexity of modern lighting requirements, coupled with the exponential growth of health-conscious design principles, necessitates advanced analytical approaches capable of processing multi-dimensional spectral data while providing actionable intelligence for lighting system optimization. This paper presents an intelligent LED spectral design system that integrates multi-objective optimization algorithms, distributed signal processing, and comprehensive evaluation techniques for circadian rhythm applications. The system combines Sequential Least Squares Programming (SLSQP) with Weighted Entropy Method (WEM) to create a robust A4 (Actor-Action-Asset-Attribute) scoring framework, incorporating Grid Search Random Forest (GSRF) algorithms for predictive modeling and spectral optimization. A comprehensive evaluation methodology enables real-time spectral analysis through statistical modeling and pattern detection using advanced optimization algorithms. The proposed framework addresses critical challenges in lighting design including spectral power distribution optimization, color rendering enhancement, and circadian effectiveness maximization. Experimental validation demonstrates superior performance with coefficient of determination R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.996, providing effective lighting solutions across diverse application contexts. The integrated approach achieves significant energy efficiency improvements compared to traditional lighting methods while providing automated circadian rhythm optimization for global lighting applications.
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V. W. Orion, P. W. Imogen · Frontiers in Management Science · 2024
In today’s industrial landscape, the pursuit of sustainability has become imperative for manufacturers worldwide. This paper explores the application of multi-objective optimization (MOO) in balancing the dual challenges of reducing carbon footprints and controlling costs in manufacturing. Multi-objective optimization involves optimizing two or more conflicting objectives simultaneously, generating Pareto-optimal solutions that represent the best possible trade-offs. The paper delves into the formulation of objectives and constraints, the selection of advanced algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the analysis of Pareto-optimal solutions. Detailed case studies across various industries, including automotive, chemical manufacturing, electronics, pharmaceuticals, and food and beverage, demonstrate the practical benefits of MOO. These case studies highlight significant advancements in both environmental and economic performance through the optimization of supply chains, production processes, energy management, and sustainable packaging. The integration of Industry 4.0 technologies further enhances the capabilities of MOO, enabling real-time data collection and dynamic optimization. The proactive adoption of MOO can drive significant advancements in environmental and economic performance, fostering a more sustainable and competitive manufacturing industry.
Cited by 1Open access
Moradiya, Parth Ishvarbhai · TRAP@NCI (National College of Ireland) · 2024
In this research, an innovative approach is presented for energy efficiency optimization in cloud datacenters via intelligent virtual machine (VM) consolidation. This work attempts to address the growing environmental and operational concerns regarding datacenter energy consumption through a multi-objective optimization framework that utilizes machine learning and predictive analytics. A methodology of combining workload prediction by LSTM networks with the classification of the server load using Random Forest algorithms to make informed consolidation decisions is also presented. It extends to incorporate energy minimization, and VM migration optimization objectives traditionally addressed, as well as CO2 emissions, workload prediction accuracy, and performance impact metric objectives. Evaluation utilizes CloudSim simulation environment extended with Python-based optimization algorithms. Adaptive learning mechanisms were shown to provide for significant improvements in energy efficiency with no sacrifice in service quality. Performance of the framework is extensively evaluated against traditional consolidation approaches using energy consumption and resource utilization as evaluation metrics, seeing substantial improvements. This research is of direct relevance for large-scale cloud providers who want to trade operational efficiency with environmental responsibility. It shows how advanced machine learning techniques and multi-objective optimization can improve datacenter operations while saving on environmental impact.
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Lin Wu, Tianhao Zhao, Zeyu Wu et al. · Expert Systems with Applications · 2026
• We proposed a many-objective multi-task model to address complex scheduling scenarios. • We propose an MTMOEA-LB to enhance knowledge transfer efficiency under task similarity. • Conduct scenario simulations and experiments using real-world data to highlight innovative. With the continuous development of industrial manufacturing, rationally scheduling multiple-source flexible orders is crucial for achieving sustainable production. However, most existing methods adopt a single-task optimization paradigm with independent modeling, overlooking the similarities among multi-source orders and the correlations between scheduling optimizations. This leads to additional waste of computational resources and the loss of prior experience. To address this issue, we propose a many-objective multi-task optimization paradigm that takes into account the correlations among multi-source flexible orders. Specifically, through the multi-task optimization paradigm, multiple flexible orders with different variable dimensions are uniformly modeled by sharing a set of objective functions, thereby enabling the joint optimization of related scheduling tasks. Secondly, to investigate the impact of tool wear on scheduling performance, an exponential function-based tool wear coefficient is introduced, and a many-objective optimization model is constructed that considers indicators such as processing time, energy consumption, load balancing, and tool wear amount. Next, considering the discrete problem properties, a many-objective multi-task evolutionary algorithm based on load balancing (MTMOEA-LB) is designed to avoid premature convergence of the multi-task optimization algorithm due to task differences. Finally, the actual production order data set of a flange enterprise is used for experimental comparison. Experimental results indicate that the proposed algorithm can output high-quality scheduling schemes quickly and effectively compared with other advanced optimization algorithms.
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Mohammed Alrasheed · Processes · 2023
Optimization algorithms have significantly evolved because of advancements in computational capacity. This increase aids in the availability of data to train various artificial intelligence models and can be used in optimizing solutions for electronic chip cooling. In the current study, such a microchannel heat sink (MCHS) is optimized using a Boron Nitride Nanotube (BNN)-based nanofluid as a coolant. Thermal resistance and pumping power are chosen as the objective functions, while geometric parameters such as the channel aspect and width ratio are used as the design variables. Multi-objective multiverse optimizer (MOMVO), an evolutionary algorithm, is used to optimize both objective functions, which are minimized simultaneously. The primary objective of this study is to study the applicability of such advanced multi-objective optimization algorithms, which have not previously been implemented for such a thermal design problem. Based on the study, it is found that the optimal results are obtained with a population size of only 50 and within 100 iterations. Using the MOMVO optimization, it is also observed that thermal resistance and pumping power do not vary significantly with respect to the channel aspect ratio, while pumping power varies linearly with the channel width ratio. An optimum thermal resistance of 0.0177 °C/W and pumping power of 10.65 W are obtained using the MOMVO algorithm.
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Fengji Luo · Membrane Technology · 2025
Background: The demand for renewable energy has catapulted wind power to the forefront of sustainable energy options. Designing wind turbine blades is a multi-objective optimization problem with conflicting objectives. Optimization methods such as the weighted sum or the goal programming method fail in this context of conflicting objectives: minimum weight, cost, and strength. The study discusses the integration of artificial intelligence algorithms with conventional design approaches for optimal blades of a 1.5MW DFIG wind turbine. Advanced computational methods are used to integrate aerodynamic efficiency, structural durability, and economic feasibility. Methods: This paper uses a broad meta-analytical approach with the integration of finite element analysis (FEA)and artificial intelligence optimization algorithms. This paper will consider three critical optimization techniques: genetic algorithms (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO). The blade performance will be considered for various operating conditions, including aerodynamic efficiency, structural integrity, and cost. This analysis will be performed based on IEC 61400 standards and new frontiers of innovative design optimization techniques. Results: Blade design optimization showed considerable improvement in using AI-driven approaches. For the GWO algorithm, better convergence is around 20% faster than the one obtained with traditional methods. This optimized design reduces weight by 8% and improves structural durability by an increase of 25% in fatigue life. For combined blade design, the maximum value of the power coefficient is 0.27, thus showing considerable improvement compared to the conventional designs. Besides, innovative material selection and design optimization could reduce the cost index by 17.6%. Conclusion: These results highlight that incorporating AI algorithms with traditional methods has significantly enhanced wind turbine blade optimization. This methodology was developed in such a way that it achieved a balanced solution for multi-objective designs, including computational efficiency. This study stresses industrial feasibility by using advanced optimization techniques for real applications, thus demonstrating their impact on the future of wind energy technological development.
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Shao Chang, Qi Zhao, Nana Pu et al. · arXiv (Cornell University) · 2026
Dynamic multi-objective optimization (DMOO) has recently attracted increasing interest from both academic researchers and engineering practitioners, as numerous real-world applications that evolve over time can be naturally formulated as dynamic multi-objective optimization problems (DMOPs). This growing trend necessitates advanced benchmarks for the rigorous evaluation of optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework features several novel components: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces, a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes, and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. This work establishes a new standard for dynamic multi-objective optimization benchmarking, providing a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems.
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Yaqing Jin, Zhixia Zhang, Liping Xie et al. · Egyptian Informatics Journal · 2023
Interval multi-objective optimization problems (IMOPs) are one of the most critical optimization problems in practical applications. However, compared to deterministic multi-objective optimization problems (MOPs), there are few researchs addressing IMOP. In addition, the uncertainty contained in the problem makes the distribution of the population more challenging. Therefore, this paper proposed a decomposition-based interval multi-objective evolutionary algorithm with adaptive adjustment of weight vectors and neighborhoods (IMOEA/D-AWN). Firstly, an interval sparsity level function (ISL) is constructed to measure the density of individuals, and a comprehensive ranking of interval sparsity ranking and interval uncertainty ranking is proposed. For the purpose of improving the distribution of the population while reducing its uncertainty, based on the above comprehensive ranking, a new adaptive adjustment weight vector strategy guided by interval elite population is designed. Besides, an adaptive adjustment neighborhoods strategy is designed. This strategy adjusts individuals' neighborhood size according to the number of iterations to improve the efficiency of evolution. Finally, the IMOEA/D-AWN is evaluated on 17 interval benchmark test problems and a collaborative computation offloading optimization problem, and compared with four advanced multi-objective evolutionary algorithms with interval parameters (IMOEAs). Experimental results show that this algorithm performs well in convergence, diversity, and uncertainty.
Cited by 8Open access
Anjali Basnyat Bista, Kim Aleksander Haukeland Paus, Isabel Seifert-Dähnn · Environmental Modelling & Software · 2025
Multi-objective optimization (MOO) methods are increasingly used to optimize the multifunctional benefits and costs of Nature-based solutions (NbS) in urban water management. However, comprehensive reviews of current research in this area remain limited. This scoping review analyzed 110 optimization studies focused on NbS in urban water management. It examined key components of the simulation-optimization (S/O) framework, including simulation models, optimization algorithms, objectives, spatial and temporal configurations. Twenty-three algorithms were assessed for their applications, strengths and limitations. NSGA-II was the most widely used, while advanced algorithms were applied in a few studies for complex, high-dimensional problems. Water quantity (87%) and costs (93%) were the most studied objectives, whereas only 11% of studies addressed other socio-environmental objectives. Various challenges in the S/O framework, such as integrating socio-environmental benefits, model complexity, uncertainties and optimal solution selection, are discussed. Future research should prioritize proper objectives selection, socio-environmental objectives integration and advanced dynamic NbS planning. • Analyzes the impact of components of simulation-optimization framework on model performance and computational complexity. • Reviews the strengths and weaknesses of optimization algorithms. • Identifies challenges encountered in the simulation-optimization framework and provides insights to resolve them. • Recommends future research on finding the appropriate objectives and on methods to integrate them into the optimization algorithm.
Cited by 3Open access
Ningkang Pan, Li Lv, Renbin Xiao et al. · Research Square · 2023
Abstract Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population; then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm.
Cited by 1Open access
Shao Chang, Qi Zhao, Nana Pu et al. · ArXiv.org · 2026
Dynamic multi-objective optimization (DMOO) has recently attracted increasing interest from both academic researchers and engineering practitioners, as numerous real-world applications that evolve over time can be naturally formulated as dynamic multi-objective optimization problems (DMOPs). This growing trend necessitates advanced benchmarks for the rigorous evaluation of optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework features several novel components: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces, a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes, and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. This work establishes a new standard for dynamic multi-objective optimization benchmarking, providing a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems.
Cited by 0Open access
Arash Heidari, Sebastian Rojas Gonzalez, Tom Dhaene et al. · arXiv (Cornell University) · 2024
Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives. However, the computation of the entire Pareto front can become prohibitively expensive, both in terms of computational resources and time, particularly when dealing with a large number of objectives. In practical applications, decision-makers (DMs) will select a single solution of the Pareto front that aligns with their preferences to be implemented; thus, traditional multi-objective algorithms invest a lot of budget sampling solutions that are not interesting for the DM. In this paper, we propose two novel algorithms that employ Gaussian Processes and advanced discretization methods to efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems. Our approach involves interacting with the decision-maker to guide the optimization process towards their preferred trade-offs. Our experimental results demonstrate that our proposed algorithms are effective in finding non-dominated solutions that align with the decision-maker's preferences while maintaining computational efficiency.
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A. M. Thompson · 2022
The design of turbomachinery components plays a crucial role in determining the efficiency, reliability, and performance of various engineering systems such as gas turbines, pumps, compressors, and fans. Computational approaches have increasingly become indispensable in the optimization of these components, providing valuable insights that are not feasible through traditional experimental methods. This paper explores the utilization of computational fluid dynamics (CFD), optimization algorithms, and machine learning techniques in the design process of turbomachinery components. Various optimization methodologies such as multi-objective optimization, surrogate models, and genetic algorithms are discussed in the context of improving the aerodynamic performance, structural integrity, and thermal efficiency of these components. The paper also highlights current challenges and future directions in the field, particularly in terms of integrating advanced computational techniques into the design lifecycle.
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HAN Meihui, WANG Peng, LI Ruixu, LIU Zhongyao · SHILAP Revista de lepidopterología · 2024
The solution of Constrained Multi-Objective Optimization(CMOP) problems aims to reasonably allocate limited search resources to satisfy constraints and optimize the objective functions. However, the increasing complexity of the problem constraints has led to significant challenges to the solution algorithm. To address this challenge, this study proposes an adaptive constrained multi-objective evolutionary algorithm based on co-evolutionary, named ACMCA. The algorithm simultaneously evolves two populations(the main population and the archive population) with complementary functions to achieve a good balance between constraint processing and objective optimization when addressing complex constraint problems. First, the main population performs a dual reproduction. In the first reproduction process, the valuable information carried by the infeasible solution is adaptively used through the dynamic fitness distribution function such that the population emphasizes the optimization of the objective function in the early stage of evolution and feasibility in the later stage. The second reproduction cooperates with the archived population to improve the convergence and maintain diversity. Subsequently, an angle-based selection scheme is proposed to update the archived population, which ensures satisfactory population diversity while maintaining the search pressure on the Pareto Front(PF). Finally, the algorithm conducts comparison experiments with five advanced Constrained Multi-Objective Evolutionary Algorithms(CMOEAs) on 33 benchmark problems. The test results demonstrate that the proposed algorithm is more advantageous than the comparison algorithms in handling various types of CMOP problems, and its efficiency is improved by an average of about 67%.
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Bilel Derbel · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2025
In this work, we address multi-objective combinatorial optimization problems, which require managing the discrete nature of the search space while simultaneously optimizing the conflicting objective functions. Local search is widely recognized as a cornerstone in the development of advanced algorithms for discrete search spaces, while evolutionary paradigms such as decomposition, dominance-based methods, and indicator-based approaches are often employed to tackle the multi-objective nature of these problems. We consider a simple combination of iterated local search with the well-established MOEA/D approach — where single-objective subproblems are generated via objective aggregation. This approach is compared to a simple evolutionary algorithm and its superiority demonstrated on a broad range of challenging binary MNK-landscapes used as benchmarks.
Cited by 2
Le Qi, Chen Lv, Tao Zhang et al. · Scientific Reports · 2026
Aviation maintenance scheduling presents complex challenges due to dynamic task arrivals, stochastic service times, and the need to balance competing objectives. To address this, we introduce a novel framework that integrates these factors into a real-time, multi-objective optimization model. Our approach combines mathematical modeling with advanced meta-heuristic algorithms, supported by new theoretical performance guarantees. We evaluate nine algorithms across 810 experimental configurations, demonstrating that our proposed methods achieve statistically significant improvements over baseline scheduling approaches. Among single-objective metrics, Adaptive Tabu Search (ATS) achieves the lowest cost at $13,072 ± $4544, while multi-objective methods provide diverse Pareto fronts with a mean hypervolume of 0.0268 (normalized scale [0, 1]), dominating significantly more of the objective space than comparative methods. The framework demonstrates the potential for significant operational cost reductions and provides a robust theoretical foundation for developing next-generation maintenance scheduling systems.
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Luca Rebuffi, Runyu Zhang, Xianbo Shi et al. · Figshare · 2025
We present an integrated system that combines advanced wavefront diagnostics with artificial intelligence (AI) to automate and optimize X-ray optics at synchrotron beamlines. This system couples real-time wavefront sensing with AI-driven control algorithms to achieve precise beam alignment, stabilization, and performance optimization. A key feature is the use of multi-fidelity transfer learning, which enables knowledge gained from both real-world beamline optimizations and ultra-realistic digital twin simulations to be effectively applied to in situ optimization. By leveraging Multi-Objective Bayesian Optimization, the system continuously refines its performance, reducing optimization time and minimizing the need for manual adjustments. Designed for seamless deployment, it operates with existing beamline hardware and provides an intuitive graphical interface. Initial deployments at the Advanced Photon Source beamlines have demonstrated its ability to enhance beam stability, improve reproducibility, and significantly streamline alignment procedures. This AI-enhanced control framework represents a significant step toward fully autonomous beamline operation in next-generation synchrotron facilities.
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