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This paper presents a multi-objective optimization approach for developing efficient and environmentally friendly Machine Learning models. The proposed approach uses Genetic Algorithms to simultaneously optimize the accuracy, time-to-solution, and energy consumption simultaneously. This solution proposed to be part of an Automated Machine Learning pipeline and focuses on architecture and hyperparameter search. A customized Genetic Algorithm scheme and operators were developed, and its feasibility was evaluated using the XGBoost ML algorithm for classification and regression tasks. The results demonstrate the effectiveness of the Genetic Algorithm for multi-objective optimization, indicating that it is possible to reduce energy consumption while minimizing predictive performance losses. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing in Internet of Things (IoT) applications has become widespread. However, it presents challenges for latency-sensitive scenarios due to data transmission to the centralized cloud structure, which leads to increased network traffic and service delays. To address this, fog computing has emerged as an intermediary layer between the cloud and IoT, ensuring low-latency interactions. A pivotal challenge within the fog computing paradigm is the service placement problem, involving assigning services to appropriate nodes, which is recognized as NP-hard. Recognizing the intricate nature of service placement, this study introduces a multi-objective optimization approach tailored for dynamic service placement within container-based fog computing environments. Considering multiple objectives is imperative due to the complex interplay of performance metrics in fog computing scenarios. A two-tier resource management framework based on Kubernetes is proposed to manage these diverse yet interrelated objectives effectively. The framework harnesses the power of the multi-objective, non-dominated sorting genetic algorithm II (NSGA-II) to reconcile conflicting objectives and facilitate optimal service placement decisions. Incorporating multi-objective optimization enables a comprehensive evaluation of service placement solutions, ensuring a balanced trade-off between latency, cost-efficiency, and energy consumption. Empirical evaluations demonstrate that the proposed method improves cost, average latency time, and energy consumption by 8% to 36% compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the inception of the Argo program, the global ocean observation network is undergoing continuous advancement, with profiling buoys emerging as pivotal components of this network, thus garnering increased attention in research. In efforts to enhance the efficiency of profiling buoys and curtail energy consumption, a teardrop-shaped buoy design is proposed in this study. Moreover, an optimization methodology leveraging neural networks and genetic algorithms has been devised to attain an optimal profile curve. This curve seeks to minimize drag and drag coefficient while maximizing drainage, thereby improving hydrodynamic performance. Simulation-based validation and analysis are conducted to assess the efficacy of the optimized buoy design. Results indicate that the drag of the teardrop-shaped buoy with a deflector decreased by 9.2% compared to pre-optimized configurations and by 22% compared to buoys lacking deflectors. The hydrodynamic profile devised in this study effectively enhances buoy performance, laying a solid foundation for ocean thermal energy generation and buoyancy regulation control. Additionally, the optimized scheme serves as a valuable blueprint for the design of ocean exploration devices. [ABSTRACT FROM AUTHOR]

This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. [Display omitted] • 4E analysis and optimization performed on a bio-waste driven polygeneration system. • Plant optimized using artificial neural network and multi-objective genetic algorithm. • The feasibility of integrating a membrane for hydrogen separation investigated. • The optimum exergy efficiency and total cost rate obtained 38.26 % and 58.17 M$/year. • Hydrogen and electricity cost of 1.49 $/kg and 0.0145 $/kWh achieved for optimal case. [ABSTRACT FROM AUTHOR]

[Display omitted] • The syndicated optimization for engine-ORC combined system is conducted. • The power, bsfc, and BSNOx of combined system are improved by coupling ANN with NSGA. • The optimum injection timing, valve phases, pump and expander speeds are obtained. • The optimized BSNOx reduces 37.16%, without the deterioration of bsfc and power. Organic Rankine cycle (ORC) could compensate for the brake power loss caused by in-engine purification by recovering exhaust energy, thus achieving lower emissions and higher power output for the engine. Aimed at identifying optimum parameters that could maximize the performance of a diesel engine-ORC combined system, this study conducts a multi-objective optimization of dynamic performance, fuel consumption, and NOx emission of the combined system, considering the engine's injection timing, intake, and exhaust phases, ORC's working fluid pump speed, and expander speed as decision variables. Initially, a validated simulation model is used to study the effect of these parameters on the performance of the combined system, indicating the necessity of optimization. Subsequently, initial datasets are obtained, with the data of decision variables obtained using the D-optimum Latin hypercube sampling method and the performance indexes calculated by the simulation model. Following these initial datasets, artificial neural network (ANN) models predicting the performance of the combined system are constructed. And the power output, brake-specific fuel consumption (bsfc), and NOx emission are optimized by coupling ANN with non-dominated sorting genetic algorithm-III. Multi-objective optimization results indicate that overly reducing NOx emissions can degrade the dynamic performance and fuel efficiency of the combined system, with the degradation of 1.85% in power output and 1.89% in bsfc as a consequence of a maximum reduction of 43.79% in Brake-specific NOx (BSNOx). Prioritizing maximum power could substantially reduce fuel consumption but weaken the effectiveness of NOx reduction, with a 4.03% improvement in power output and a reduction of 3.87% and 16.48% in bsfc and BSNOx, respectively. A balanced approach considering both dynamic performance and NOx emission is the most suitable for enhancing performance with emission control in mind. This could ensure a 37.16% reduction in BSNOx, maintaining comparable power and fuel consumption. This study offers valuable insights for future advancements in engine performance. [ABSTRACT FROM AUTHOR]

Workload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%. [ABSTRACT FROM AUTHOR]

• The gradient distributed Tesla cold plate is proposed. • Hydro-thermal performance is optimized using kinging model and NSGA-II. • Mass flow rate poses a most significance on energy efficiency. • A maximum reduction of 75.7% for pressure drop is obtained. • Optimal case achieves module maximum temperature difference of 3.6 °C. To address energy-efficient cooling of battery module at a high discharge rate, this work presents a novel gradient distributed Tesla cold plate. Multi-objective optimization is performed to achieve optimal thermal performance and energy efficiency using coupled battery-cold plate simulations, kriging surrogate model and second non-dominated sorting genetic algorithm. Both cold plate structure and operating parameters are considered with objectives of maximum temperature, temperature uniformity and pressure drop. The results demonstrate that the mass flow rate poses a most significance on hydro-thermal performance, followed by channel depth and Tesla valve distance. A conflicting demand for the mass flow rate and channel depth is observed to achieve best hydro-thermal performance. In accordance with Pareto frontier solution, the improvement of pressure drop is most significant with a maximum reduction of 75.7% compared to base case. A moderate mass flow rate with increasing channel depth is recommended as the optimal strategy, yielding moderate liquid convection percentage (51.8%), cooling efficiency (2 3 3) and the highest Nu (6.7). Moreover, present gradient distribution of fractal inlet and Tesla unit contributes to high heat transfer coefficient zones along flow direction, achieving favorable temperature uniformity with maximum temperature difference of module middle section lower than 4 °C. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta -models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta -model provides reliable estimation with an average RMSE and MAE of 2.604 m m / m i n and 3.402 m m / m i n for TBM's penetration rate(O 1), 0.0211 and 0.0324 for over/under excavation ratio (O 2), and 15.512 k w h and 23.088 k w h for energy consumption (O 3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O 1 to O 3 , respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O 1 to O 3 , respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation. [ABSTRACT FROM AUTHOR]