Multi-objective Optimization Framework Research Articles

Lightning Protection With a Differentiated Arrester Configuration for Distribution Networks Using a Multi-Objective Optimization Procedure

In addition to insulator flashover, lightning damage of surge arresters (SAs) is also considered in evaluating the lightning performance of a distribution network. This paper presents a novel multi-objective optimization framework for designing differentiated SA protection, by minimizing both the flashover number and SA damage rate. An artificial neural network and an energy-distribution surrogate model are developed for the efficient evaluation of these two targets in an optimization process. The samples for building these models are generated with a hybrid simulation approach together with a Monte Carlo method. It is concluded that the surge energy into an SA generally follows a log-normal probabilistic distribution. The mean of energy absorption decays exponentially with the distance to the stroke location. Differentiated SA configurations for a 10kV distribution network are then identified by balancing the need of minimizing these two targets. It is found that when increasing the SA number, the damage rate becomes dominant and SAs are recommended to be located close to the dead-end poles in sub-feeders. Otherwise, the annual flashover number becomes dominant and SAs tend to be located around the junction poles on the main feeder. When the annual flashover number is primarily addressed, the pole-group selection strategy is the best one due to its simplicity and better performance. However, if both the annual flashover number and damage rate are of concern, the proposed optimization procedure is recommended to achieve a balanced lightning performance

Long-term sustainability and resilience enhancement of building portfolios

The role of community building portfolios in socioeconomic development and the growth of the built environment cannot be overstated. Damage to these structures can have far-reaching consequences on socioeconomic and environmental aspects, requiring a long-term perspective for recovery. As communities aim to enhance their resilience and sustainability, there is a cost burden that needs to be considered. To address this issue, this paper proposes a community-level performance enhancement approach that focuses on optimizing the long-term resilience and sustainability of community building portfolios, taking into account recurrent seismic hazards. A Gaussian process surrogate-based multi-objective optimization framework is utilized to optimize the cost objective while considering performance indicators for resilience and sustainability. The proposed framework involves using performance-based assessment methods to evaluate the socioeconomic and environmental consequences under stochastic and recurrent seismic hazard scenarios. These evaluated indicators are then used to efficiently optimize the community resilience and sustainability, taking into account the retrofit costs. Finally, approximate Pareto-optimal solutions are extracted and utilized for decision-making. In summary, this paper presents a novel approach for optimizing the long-term resilience and sustainability of community building portfolios by considering recurrent seismic hazards. The proposed framework incorporates performance-based assessment methods and multi-objective optimization techniques to achieve an optimal balance between cost, resilience, and sustainability, with the ultimate goal of enhancing community well-being and decision-making in the face of seismic hazards.

A data-driven robust design optimization method and its application in compressor blade

The probability-based robust optimization methods require a large amount of sample data to build probability distribution models of uncertain parameters. However, it is a common situation that only scarce sampled data are available in practice due to expensive tests. This study proposes a data-driven robust optimization framework by embedding a novel uncertainty quantification (UQ) method, which can quantify the uncertainty based on the statistical moments of scarce input data. The computational robustness and accuracy of the developed UQ methods are validated. Then, the data-driven multi-objective optimization framework is applied to improve the mean performance and aerodynamic robustness of a two-dimensional compressor blade with real stagger angle errors. Uncertainty analysis shows that there is a probability of 47.55% to deviate from the nominal total pressure loss coefficient by more than 1% for the actual performance values at high positive incidence i=7° condition. In the optimization process, the total pressure loss coefficient is selected as the objective function, while the static pressure ratio is used as a constraint. The Gaussian process regression model is trained to improve the robust optimization efficiency. The robust optimization is conducted under the most sensitive conditions. Optimized results indicate that compared with the nominal blade, the mean performance of the selected robust blades is increased by 10.9%, 8.56%, and 0.83%; the performance dispersion is decreased by 19.0%, 24.8%, and 35.3%, respectively. The optimized results can provide useful references for the robust design of compressor blades.

Improving robustness of water supply system using a multi-objective robust optimization framework

Due to the uncertainty of rainfall and water demand, the water supply of various participants has been challenged in such a way that this challenge has accelerated the failure of water supply system. Thus, this study proposes a multi-stage Adjustable Robust Optimization integrated to the multi-objective programming framework to drive water supply system to the failure safety zone and thereby improve robustness of system under different scenarios. Indeed, Adjustable Robust Optimization framework is applied to investigate the two uncertain factors of rainfall and water demand. A real arid area of Sistan basin in southeastern Iran is considered to analyze the proposed multi-objective programming model. Next, various comparative feasibilities under different levels of uncertainty are carried out to examine the robustness status in more detail. In the following, due to the deterioration of climatic patterns in the coming years, some managerial insights are highlighted. According to the final outputs, the domestic sector has reached more optimal value compared to that of the agricultural and industrial participants in all objectives due to less water intake, and as a result, it has a significant impact on the robustness of water supply system.

An extended fuzzy decision variables framework for solving large-scale multiobjective optimization problems

In large-scale multiobjective optimization, the huge search space poses a great challenge to the convergence search of existing evolutionary algorithms. A fuzzy decision variables (FDV) framework for large-scale multiobjective optimization involves a complex parameter tuning process, and the convergence efficiency is moderate. Therefore, we propose an extended fuzzy decision variables (EFDV) framework based on linear search and the non-dominated rate (the proportion of non-dominated individuals in the whole population) to solve large-scale multiobjective optimization problems. First, guiding solutions are sampled from the central region to provide more possible search directions in large-scale decision space. In addition, fuzzy evolution or precise evolution is determined according to the non-dominated rate. When the non-dominated rate is low, the convergence of the whole population is relatively poor. Then a higher degree of fuzzy search is carried out, and the individuals with poor convergence are reversed to increase the population's diversity and enhance the effectiveness of searches. Finally, by comparing experiments on several large-scale multiobjective test suites with 500–5000 decision variables, the efficiency of the EFDV is confirmed. According to experimental results, the EFDV can significantly enhance the performance and computing effectiveness of multiobjective optimizers when used for large-scale multiobjective optimization.

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

AbstractDielectric elastomers are a promising technology for wave energy harvesting. An optimal system operation can allow maximizing the extracted energy and, simultaneously, reducing wear that would lead to a reduction in the wave harvester lifetime. We pursue a model‐based optimization approach to identify optimal controls for wave energy harvesters based on dielectric elastomers. First, a direct method is used for time‐discretization of the dielectric elastomer wave energy harvester in the optimal control problem. The two conflicting objectives are considered in a multiobjective optimization framework. Considering a periodic, sinusoidal wave excitation, the optimal solution shows turnpike properties for the optimal periodic mode of operation. However, since real wave motion is neither monochromatic nor predictable on longer time horizons, further extensions are pursued. First, we introduce a stochastic wave excitation. Second, an iterative model‐predictive control scheme is designed. Due to multiple objectives, the control scheme has to include an automated adaption of the corresponding priorities. Here, we propose and evaluate a heuristic rule‐based adaption in order to maintain the damage below target levels. The approach presented here might be used in the future to guarantee for autonomous operation of farms of wave energy harvesters.

Optimized design and integration of energy storage in Solar-Assisted Ground-Source Heat Pump systems

The integrated use of multiple renewable energy sources to increase the efficiency of heat pump systems, such as in Solar Assisted Geothermal Heat Pumps (SAGHP), may lead to significant benefits in terms of increased efficiency and overall system performance especially in extreme climate contexts, but requires careful integrated optimization of the different system components. In particular, thermal storages take a fundamental role in optimizing the integration of renewable energy sources and the system operation. This work investigates the potential design optimization of a SAGHP system in a mountain site by exploring many different alternatives to optimize the mutual relationship between the solar field, the geothermal field and the water thermal storages. This is done through an original simulation-based multi-objective optimization framework considering energy efficiency and economic feasibility, which allows appraising the impact of the different design alternatives on the overall system performance and on the dynamics of the different system components. Results identify a set of optimized system configurations that optimize the integrated exploitation of the different thermal sources showing a potential increase of the overall system performance leading to 34% lower global cost compared to the initial design. High robustness of the optimal design solutions is reported with respect to the current context of high economic uncertainty.

AI-Assisted optimisation of green concrete mixes incorporating recycled concrete aggregates

Maximising the content of supplementary cementitious materials as a partial replacement for Portland cement and using recycled concrete aggregates as a full or partial replacement for coarse aggregates are among the widely adopted strategies in the design of green concrete. However, the synergetic implications of adopting both approaches on concrete properties add complexity to the design of green concrete mixes. This paper proposes a novel AI-based multi-objective optimisation framework addressing the lack of a systematic method to formulate these concretes and solves the common multi-objective mix design problems. The framework is developed for the mix proportioning of green concrete mixes containing recycled concrete aggregates and commonly used supplementary cementitious materials while considering the desired compressive strength. The proposed optimisation model leverages a dataset of about 2,120 mixes and employs an extreme gradient boosting machine to model the compressive strength requirements as a mechanical constraint throughout the optimsiation process. Moreover, a set of key environmental objectives, including minimising global warming, acidification, fossil fuel depletion potentials, and the cost of production, are targeted. The effectiveness of the framework is validated through case studies across different strength ranges. The results indicate a significant improvement in environmental sustainability indicators, including a 19.6% and 21.6% reduction in global warming and acidification potentials, besides considerable cost savings of up to 23.7% without compromising the mechanical performance of concrete.

Multi-objective optimization of a CO2/H2O capture-based ventilation and air conditioning system

Developing energy-efficient ventilation and air-conditioning (VAC) strategies is pivotal for achieving green buildings’ triple merits of high indoor air quality (IAQ), low CO2 emission, and low energy consumption. While adsorption-based carbon capture technologies show promising potential in improving IAQ and reducing AC energy consumption by directly removing excess CO2/H2O and recirculating post-captured air in buildings, current studies lack in-depth investigation into simultaneously maximizing CO2 removal capacity and minimizing AC energy consumption. This trade-off, hindering the broad application of this technology, is rendered by the intricate interplay between indoor conditions, system configuration, and more importantly, the adsorbent materials. To circumvent this trade-off, this study proposes an NSGA-II-based multi-objective optimization model on a solar-driven CO2/H2O capture-based VAC system for optimizing its techno-energetic performances. This analysis maps the green buildings’ merits into five constrained objectives and fully optimizes them by considering a wide spectrum of decision parameters. This analysis automatically optimized the trade-off between conflicting objectives for both studied adsorbents to various extents. While maintaining the same IAQ level, a 74% and 59% improvement in maximal captured CO2 mass can be achieved for Mg-MOF-74 and Zeolite13X. Compared with Mg-MOF-74, Zeolites 13X performed 55% worse in maximal CO2 removal, but 82% better in maximal energy-saving potential due to higher cyclability, stability, and lower specific energy consumption. Additionally, the proposed multi-objective optimization framework could be applied to other adsorbent materials and capture methods to guide the optimal design of CO2/H2O capture-based VAC systems for green building development.

An adaptive multi-objective joint optimization framework for marine hybrid energy storage system design considering energy management strategy

The electric propulsion ship with the hybrid energy storage system (HESS) has environmental friendliness and significant advantages in terms of low fuel consumption. Due to the high maneuverability and load fluctuation of vessels, marine HESS design problem is nonlinear and multi-objective. Aiming at HESS design problem with complex working conditions during vessel practical operation process, and since there is a strong coupling between HESS and power allocation, in this paper an adaptive multi-objective joint optimization framework (AMJOF) for HESS design considering energy management strategy (EMS) is proposed. A multi-objective joint optimization method is introduced into the framework to achieve strong working condition adaptability and reach the most suitable energy management for marine HESS design with the lowest investment cost and minimum battery degradation. In addition, based on adaptive segmentation and frequency control, a novel EMS is proposed, which achieves efficient power allocation, and the evaluation indicator is introduced to quantify the performance of different optimization. The results indicate that the AMJOF can achieve excellent performance and find the optimal solution for operation of the ship.

Multiobjective optimization and analysis of low-temperature district heating systems coupled with distributed heat pumps

Developing district heating can help decarbonizing energy systems. Modern concepts of district heating revolve around low operational temperature to reduce heat losses and enhance the efficiency of the supply plants with technologies such as cogeneration, condensing boilers or geothermal heat pumps. However, temperature reduction in district heating networks may be limited by technical requirements on the building side. Booster heat pumps on the end-users’ side can be a solution to bridge the network-building temperature gap. However, the current state-of-the-art on such systems does not provide ways for optimizing in real-time the operating temperature, which is needed to fully exploit its benefits, and proposed control strategies focus solely on reducing costs without considering environmental aspects. This article proposes a new multi-objective optimization framework to determine the real-time supply temperature for low-temperature district heating with booster heat pumps. The optimization approach uses rolling horizon and both operating costs and GHG emissions are minimized simultaneously. The model on which the optimization relies includes efficiency curves for equipment and water transport delays in the network. Many scenarios are simulated to understand how key factors affect the optimal supply temperature, such as the fuel type (i.e., wood chips or natural gas), price of electricity, emission factor of electricity, and relative weight of costs and emissions in the objective function. Results show that the proposed framework can indeed reduce costs and emissions, but the viability of the system depends strongly on the above-mentioned factors. In the case-study with typical electricity costs and emissions for Quebec (Canada), the average operating temperature could be reduced to 42 and 66 °C depending on the type of boilers in the system. While emissions were always smaller than with a conventional network, operating costs could be higher (with wood chip boiler) or lower (with natural gas boiler). This work can contribute to the deployment of efficient low temperature networks.

Inerter-based damping isolation system for vibration control of offshore platforms subjected to ground motions

Offshore structures are critical infrastructure in the field of ocean engineering, which may under the threat of earthquake shaking, especially when they are located in the high-risk seismic zones. This paper proposes using an innovative inerter-based damping isolation system (IDIS) for the seismic protection of offshore platforms. The concept of IDIS is firstly proposed by integrating the conventional bearings with inerter-based dampers (IDs). An analytical model of the system is established, and the corresponding equilibrium equations of motion are derived. Without loss of generality, the deck acceleration, displacement on the jacket cap, and isolating layer deformation are chosen as the performance objectives, and parametric analyses are performed to investigate the influences of the key parameters of IDIS on each performance objective. A performance-based multi-objective optimization framework is then developed for determining the optimal parameters of IDIS. The control effectiveness of IDIS is demonstrated in the frequency domain and compared to that of the traditional damping isolation system (DIS). Finally, case studies are performed to further investigate the performance of IDIS subjected to natural seabed ground motions. The results show that properly optimized IDIS is more effective than the conventional DIS in terms of not only the structural responses but also the deformation of the isolating layer. The proposed IDIS can be an attractive and effective alternative to the conventional DIS in the vibration control of offshore platforms.

Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities

The combination of low-carbon electricity and electric vehicles brings considerable economic and environmental benefits but also introduces challenges due to the complexity and uncertainty of system synergies in smart cities. To fully exploit the advantages of photovoltaic power generation and electric vehicles and to release the potential of electric vehicles as distributed energy storage facilities, this paper develops a multi-objective robust optimization framework that accounts for the benefits of multiple parties of smart charging and discharging systems and depicts a bounded uncertainty set based on partial statistical information from real data. The original model is scalarized and linearized using efficient methods such as max-ordering scalarization and the robust augmented weighted Tchebycheff to facilitate the solution. Moreover, a smart charging and discharging scheduling strategy based on a convergent demand response strategy is proposed to achieve better demand-side management. A case study, based on real data from Car2go and the solar radiation intensity in Portland, Oregon, shows that (1) The methodological framework effectively addresses the smart scheduling issue that considers the interests of multiple parties in complex systems with uncertainty, in contrast to conventional methods. (2) The designed convergent demand response strategy promotes photovoltaic power generation and charging demand synergy and achieves regulation effects such as peak shaving and valley filling. (3) The proposed method and strategy yield good results in multiple aspects, such as charging costs, load regulation, and clean energy utilization, while enhancing the economic and environmental benefits.

Environmental-thermal-economic performance trade-off for rural residence retrofitting in the Beijing–Tianjin–Hebei region, Northern China: A multi-objective optimisation framework under different scenarios

Environmental-thermal-economic performance trade-off for rural residence retrofitting in the Beijing–Tianjin–Hebei region, Northern China: A multi-objective optimisation framework under different scenarios

A Multi-Objective Optimization Framework for Electric Vehicle Charge Scheduling With Adaptable Charging Ports

A Multi-Objective Optimization Framework for Electric Vehicle Charge Scheduling With Adaptable Charging Ports

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

This research aimed to study the effects of individual components on the physicochemical properties of systems composed of surfactants, polymers, oils, and electrolytes in order to maximize the recovery efficiency of kerosene while minimizing the impact on the environment and human health. Four independent factors, namely anionic surfactant sodium dodecylbenzene sulfonate (X1) (SDBS), oil (X2) (kerosene), water-soluble polymer poly(ethylene glycol) (X3) (PEG), and sodium chloride (X4) (NaCl), were studied using the full factorial design (FFD) model. Four output variables, namely conductivity (Y1), turbidity (Y2), viscosity (Y3), and interfacial tension (IFT) (Y4), were taken as the response variables. All four FFD models have high coefficients of determination and low errors. The developed models were used in a multi-objective optimization (MOO) framework to determine the optimal conditions. The obtained optimal conditions are X1 = 0.01, X2 = 50, X3 = 5, and X4 = 0.1, with an error of 0.9414 between the predicted and experimental objective function values. This result shows the efficiency of the model developed and the system used for the recovery of kerosene, while also having a positive effect on the protection of the environment.

Multi-objective optimization for improving machining benefit based on WOA-BBPN and a Deep Double Q-Network

To scientifically and reasonably set and adjust the machining parameters, and improve the machining benefit of the batch machining process including machining quality, performance and efficiency, this paper proposes a low cost and high accurate multi-objective optimization framework by integrating intelligent technologies from three perspectives of physical experiment design, objective function construction and multi-objective optimization. In this framework, multilayer design (MLD) which is a computer experiment design method with low redundancy, good uniformity and filling is applied to the machining physics experiment design to reduce the experiment cost. Whale optimization algorithm (WOA) optimized backpropagation neural network (BBPN) is utilized to construct the optimization objective functions to improve the accuracy of functions. And Deep Double Q-Network (DDQN) is employed to achieve multi-objective optimization of machining quality, efficiency, and performance. The effectiveness of MLD, WOA-BBPN and DDQN in multi-objective optimization of machining process are verified by comparisons of common modeling and multi-objective optimization algorithms based on the milling data. These techniques provide a good guidance for the selection of machining parameters to improve the machining benefit and demonstrates some advantages. The research of this paper also offers a good example of applying intelligent technology in machining.

Thermal performance optimisation of Pavement Solar Collectors using response surface methodology

Recent studies have highlighted the factors influencing the thermal performance of Pavement Solar Collectors (PSC), such as thermophysical properties of materials, geometrical specifications, and operational conditions. The present study introduces a new approach to investigating the impact of various parameters on the long-term performance of PSCs.The Response Surface Methodology (RSM) is used to optimise the experimental design by reducing the number of simulations resulting from the combination of several design parameters and ample design space. Hence, the proposed PSC system design aims to: i) assess the heat extraction capacity; ii) investigate the ability to diminish the asphalt surface temperature (STR); and iii) determine the reduction in asphalt layers’ rutting potential (RTR), through a coupled RSM and Finite Element (FE) simulation framework.The proposed statistical prediction regression models for heat harvesting capacity, STR, and RTR, adequately represent the experimental data with predicted R2 values above 0.95. The pipe spacing, flow rate, and inlet supply temperature show a high sensitivity to the objective functions, while other parameters display a less sensitive response. Finally, a multi-objective optimisation framework using the NSGA-II is proposed to seek a Pareto front solution in the design space, considering different (or equal) weights for the objective functions.

Simultaneous aerodynamic and structural optimisation of a low-speed horizontal-axis wind turbine blade using metaheuristic algorithms

AbstractThis work presents a concurrent design and multi-objective optimisation framework of horizontal axis wind turbine blades, made of composite material, for low wind speed. The optimisation model aims to minimise the structural mass of the blade whilst simultaneously maximising the turbine power output, subjected to three constraints viz. blade tip deflection, and Tsai-Hill and von Mises criteria. The design variables are blade shape and details of the internal blade structure. The control points and polynomial interpolation technique were adopted to determine the blade shape while the airfoil types at blade sections remained fixed. The internal blade structure design variables include the thickness of ribs and spars and the carbon fibre thickness and orientations. The blade element momentum approach is utilised to calculate turbine power and structural loads, whereas a finite element method is employed for structural analysis. Twelve multi-objective metaheuristics algorithms are used to solve the proposed multi-objective optimisation problem while their performance is investigated. The results obtained show that the multi-objective cuckoo search algorithm is the most efficient method. This study is said to be the baseline for a future study on multi-objective optimisation which combines two design stages of the composite low-speed wind turbine blades.

Simulation-based multi-objective optimization framework for sustainable management of coastal aquifers in semi-arid regions

Groundwater is a strategic source of water supply, especially in arid and semi-arid coastal regions. Growing demand, along with scarce water sources, may impose intense pressure on this precious resource. This pressure will degrade water quality for future use and cause social inequality, despite supplying current needs. A novel sustainable management model for water allocation is developed to address these interconnected concerns in coastal aquifers. Three aspects of sustainable development are considered: groundwater quality with total dissolved solids (TDS) indicator for the environmental part, gross value added from water for the economic efficiency, and the Gini coefficient for social inclusion and equity. The problem is solved with a simulation-based multi-objective optimization framework using a numerical variable-density simulation code and three approved evolutionary algorithms, NSGA-II, NRGA, and MOPSO. The obtained solutions are integrated to enhance the solutions' quality by using each algorithm's strengths and dominated members' elimination. In addition, the optimization algorithms are compared. The results showed that NSGA-II is the best in terms of solutions quality, with the least number of total dominated members (20.43%) and a 95% success rate of obtained Pareto front. NRGA was supreme in finding extreme solutions, the least computational time, and diversity, with an 11.6% higher diversity value than the second competitive NSGA-II. MOPSO was the best in spacing quality indicator, followed by NSGA-II, showing their great arrangement and evenness in obtained solution space. MOPSO has the propensity for premature convergence and needs more stringent stopping criteria. The method is applied to a hypothetical aquifer. Still, the obtained Pareto fronts are determined to assist decision-makers in real-world coastal sustainable management problems by illustrating existing patterns among different objectives.