A machine learning-driven multi-objective optimization framework for advanced microchannel heat sinks with gradient rib-pin fin arrays

Radiation therapy treatment planning is inherently a multiobjective problem, aiming to obtain the best tradeoffs between irradiating the tumor with the prescribed dose and sparing as much as possible the surrounding healthy organs. Many different multiobjective approaches have been proposed for the optimization of radiation intensities for fixed beam irradiation directions. However, multiobjective beam angle optimization is seldom considered. The purpose of this paper is to introduce a new multiobjective optimization framework that explicitly and simultaneously considers the optimization of intensities and also beam directions. Whilst multiobjective optimization of radiation intensities considering a fixed set of beam directions gives rise to a single Pareto front, beam angle optimization gives rise to the appearance of multiple Pareto fronts, each one associated with a given beam angle set. Our framework proposes a beam angle set choice based on the evaluation of non-dominated solutions belonging to different Pareto fronts, using a tree-based approach and a performance indicator to assess the quality of each Pareto front. The proposed approach, illustrated by head-and-neck cancer cases, allows for more flexibility in the calculation of solutions and a better understanding of the existing compromises between different objectives.

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The transition from fossil fuels to renewable energy demands energy systems that are not only technically and economically sound but also environmentally and socially sustainable. This study proposed a novel multi-objective optimisation (MOO) framework for the design of a grid-connected hybrid renewable energy system (GC-HRES) that explicitly integrated Job Creation (JC) as a fourth objective along with traditional technical, economic, and environmental objectives, such as Loss of Power Supply Probability (LPSP), Cost of Energy (COE), and Renewable Energy Fraction (REF). Prior studies often treated JC as a post-optimisation metric, while this study incorporated JC into the MOO using employment technology-specific factors. Metaheuristic algorithms, Multi-Objective Particle Swarm Optimisation (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were applied to find the optimal number of solar panels, wind turbines, and battery banks under scenarios which excluded and included JC. The results demonstrated that including JC reshaped the Pareto front, revealed new objective compromises and led to diverse configurations. MOPSO favoured solutions with higher JC and REF at the expense of cost and reliability, while NSGA-II achieved more balanced, cost-effective and reliable solutions with competitive JC values. Additional constraint sensitivity analysis further demonstrated the influence of battery bank constraints on solution feasibility. The results highlighted that integrating JC into the optimisation framework enriched design possibilities and fostered a more inclusive transition to renewable energy with a focus on GC-HRESs. This paper provided a flexible and replicable MOO framework that sets the foundation for socially attuned GC-HRES designs that align with broader sustainability goals.

In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models based on the Multi-Species Approximation Model (HAM-MSAM) is proposed to meet the requirement for high fitting accuracy. Subsequently, this study introduces a HAM-MSAM-based Approximation-Based Global Multi-Objective Optimization Design (ABGMOOD) strategy. This strategy is employed in the multi-objective optimization of the rear seat of a passenger car. HAM-MSAM was constructed from an experimentally validated finite element model and a training set generated through experimental design. The advantages of HAM-MSAM in capturing the highly nonlinear response under seat crash conditions were validated through comparison with hybrid model construction methods reported in existing literature. Finally, the optimization results obtained by the ABGMOOD strategy were compared to those of the classical local multi-objective optimization strategy, demonstrating the substantial advantages of the ABGMOOD optimization scheme in economy and weight reduction. In addition, the safety of the rear seats is slightly lower than that of the local optimization scheme but remains in compliance with regulatory requirements. The final optimized rear seat demonstrates notable improvements in safety, economy, and weight reduction, validating the feasibility of the ABGMOOD strategy and providing valuable insights for similar engineering optimization challenges.

Multi-objective optimization strategy for industrial catalytic cracking units: Kinetic model and enhanced SPEA-2 algorithm with economic, CO2, and SO2 emission considerations

We propose a fairness-constrained curriculum adaptation framework for AI-enhanced education that integrates natural language processing (NLP) with multi-objective optimization to dynamically tailor learning materials while ensuring cultural responsiveness and ethical alignment. The proposed method employs a Transformer-based language model to analyze student-generated text, extracting latent cultural and demographic features, which are then clustered using a fairness-sensitive algorithm to mitigate biased representations. These features are combined with conventional performance metrics to formulate a multi-objective optimization problem that simultaneously maximizes pedagogical effectiveness and minimizes disparities across demographic groups. The optimization framework incorporates differentiable fairness constraints, such as equalized odds, to ensure equitable learning outcomes while adhering to institutional guidelines. Moreover, the system interfaces with existing educational platforms through input and output substitution mechanisms, enabling seamless integration with conventional modules. A key novelty lies in the coupling of NLP-driven cultural analysis with constrained optimization, which provides a transparent and auditable approach to curriculum adaptation. Furthermore, the framework includes a human-in-the-loop component, allowing educators to override recommendations and refine the system iteratively. Experimental validation demonstrates that the method achieves superior fairness-performance trade-offs compared to baseline approaches, offering a scalable solution for equitable education. The work contributes to the growing discourse on ethical AI in education by addressing both technical and societal challenges through a mathematically rigorous yet interpretable framework.

• Multi-objective optimization applied to achieve NZEB in Prague. • Integration of passive, active, and renewable strategies. • Energy savings drop from 70 % to 32 % under future climates. • Emphasis on adaptive, resilient building designs. • NZEB strategies need regular optimization for future climates. This study applies a Multi-Objective Optimization (MOO) framework to enhance the energy performance of a typical high-rise residential building in Prague and assess its resilience under future climate change scenarios. A total of 18 passive strategies—including external wall and roof insulation (0.08–0.20 m), optimized Window-to-Wall Ratios (10–90 % by orientation), triple low-E glazing, and dynamic shading overhangs—were evaluated alongside two active strategies (heating and cooling COP optimization) and three renewable strategies (roof-mounted photovoltaic (PV) panels with tilt, spacing, and orientation optimization). Results show that integrating these strategies can reduce annual energy demand by up to 68.7 % under current climate conditions and by 56 %, 42 %, and 32 % in the 2020s, 2050s, and 2080s, respectively. The optimized configuration maintained acceptable indoor comfort, with Predicted Percentage Dissatisfied (PPD) values reduced by up to 18 % compared to the baseline. PV panels generated 53,875 kWh annually under current conditions, covering 69 % of thermal and electrical demand, though coverage declined under future climate scenarios due to rising cooling loads. These findings demonstrate that a comprehensive integration of passive, active, and renewable strategies can significantly improve building performance and provide a resilient pathway toward Near-Zero Energy Buildings (NZEBs) under changing climatic conditions.

With the increasing amount of distributed generation (DG) integrated into electrical distribution networks, various operational problems, such as excessive power losses, over-voltage and thermal overloading issues become gradually remarkable. Innovative approaches for power flow and voltage controls are required to ensure the power quality, as well as to accommodate large DG penetrations. Using power electronic devices is one of the approaches. In this paper, a multi-objective optimization framework was proposed to improve the operation of a distribution network with distributed generation and a soft open point (SOP). An SOP is a distribution-level power electronic device with the capability of real-time and accurate active and reactive power flow control. A novel optimization method that integrates a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and a local search technique – the Taxi-cab method, was proposed to determine the optimal set-points of the SOP, where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives. The local search technique is integrated to fine tune the non-dominated solutions obtained by the global search technique, overcoming the drawback of MOPSO in local optima trapping. Therefore, the search capability of the integrated method is enhanced compared to the conventional MOPSO algorithm. The proposed methodology was applied to a 69-bus distribution network. Results demonstrated that the integrated method effectively solves the multi-objective optimization problem, and obtains better and more diverse solutions than the conventional MOPSO method. With the DG penetration increasing from 0 to 200%, on average, an SOP reduces power losses by 58.4%, reduces the load balance index by 68.3% and reduces the voltage profile index by 62.1%, all compared to the case without an SOP. Comparisons between SOP and network reconfiguration showed the outperformance of SOP in operation optimization.

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it's critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model. In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of "approximating the underlying function" rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments.

Wireless sensor nodes are battery-powered communication devices. Their limited capabilities have imposed various constraints to the system design of wireless sensor networks (WSNs). These constraints are interrelated, and are usually in conflict with each other. Clustering is often used to reduce energy consumption in WSNs. However, an arbitrary selection of clustering parameters may lead to severe degradation in other aspects, such as extra delays in data collection processes (DCPs). In this paper, a multi-objective optimization (MOO) framework for cluster-based WSNs is proposed. The proposed framework considers both the energy consumption and the duration of a DCP as its objective functions. Simulation results show that networks optimized using the proposed framework can obtain reasonable trade-offs between the two objectives. Nevertheless, the networks optimized using the proposed framework can obtain non-dominated solutions that cannot be achieved by using energy-aware clustering algorithms.

Floating offshore wind energy will play a key role in the clean energy transition. The number of large-scale wind farm projects is growing in regions like Northern Europe, the East Coast of the U.S., and the Mediterranean Sea. Offshore wind farms face fewer layout constraints, as they can be situated in vast, open sea areas. Turbines are often arranged in simple layouts, such as grid patterns, but this can cause significant annual energy production (AEP) losses due to wake–rotor interaction. Increasing spacing can mitigate this effect, but it may not always be feasible due to marine space limitations or higher costs for cabling and maintenance. This paper introduces a multi-objective wind farm optimization framework using a non sorted genetic algorithm (NSGA II) to minimize costs and maximize AEP. The method is applied to two case studies in the Mediterranean Sea, assuming 15 MW wind turbines. Case A is located off the coast of Civitavecchia, and case B in the Gulf of Squillace. AEP evaluation is performed with the open-source library FLOw Redirection and Induction in Steady-State (floris), while optimization is done using pymoo. In case A, the layout characterized by the lowest levelized cost of energy (LCOE) features 16 turbines, achieving an AEP of 709 GWh, an LCOE of 112.06 €/MWh, and wake losses of 2.6%. Meanwhile, in case B, the layout with the lowest LCOE consists of 19 turbines, achieving an AEP of 1140 GWh, an LCOE of 80.82 €/MWh, and wake losses of 4%.

BackgroundProtein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and protein docking.ResultsWe propose a novel method to compare the protein structures in an accurate and efficient manner. Such a method can be used to not only reveal divergent evolution, but also identify circular permutations and further detect active-sites. Specifically, we define the structure alignment as a multi-objective optimization problem, i.e., maximizing the number of aligned atoms and minimizing their root mean square distance. By controlling a single distance-related parameter, theoretically we can obtain a variety of optimal alignments corresponding to different optimal matching patterns, i.e., from a large matching portion to a small matching portion. The number of variables in our algorithm increases with the number of atoms of protein pairs in almost a linear manner. In addition to solid theoretical background, numerical experiments demonstrated significant improvement of our approach over the existing methods in terms of quality and efficiency. In particular, we show that divergent evolution, circular permutations and active-sites (or structural motifs) can be identified by our method. The software SAMO is available upon request from the authors, or from and .ConclusionA novel formulation is proposed to accurately align protein structures in the framework of multi-objective optimization, based on a sequence order-independent strategy. A fast and accurate algorithm based on the bipartite matching algorithm is developed by exploiting the special features. Convergence of computation is shown in experiments and is also theoretically proven.

This paper develops a new framework to coordinate the charging process of plug-in hybrid electric vehicles (PHEVs) in the context of energy hubs. Attempts are focused not only on the impact evaluation of PHEVs on the system technical performance, but also customers' preferences on the charging patterns. PHEVs coordinator agent (PCA) is proposed to run a multiobjective optimization (MO) framework for the optimal charging patterns of PHEVs from the vehicle owners' and system operator's viewpoint. Wind energy utilization for PHEVs charging, energy costs at the hub input layer, and customers' convenience are all taken into account as the optimization objectives. Due to its promising performance in dealing with MO problems, the multiobjective particle swarm optimization method is employed to evaluate the optimization problem. The 2-point estimation method is used to model the existence uncertainties. The proposed framework is applied to the modified IEEE 34-node test system, and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world scenarios.

A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning