Multi-objective Optimization Approach Research Articles

Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions.

Clustering-based multi-objective optimization considering fairness for multi-workflow scheduling on clouds

Distributed computing, such as cloud computing, provides promising platforms for orchestrating scientific workflows' tasks based on their sequences and dependencies. Workflow scheduling plays an important role in optimizing concerned objectives for distributed computing, such as minimizing the makespan and cost. Many researchers have focused on optimizing a specific single workflow with multiple objectives. Currently, there are few studies on multi-workflow scheduling, with most research focusing on objectives such as cost and makespan. However, multi-workflow scheduling requires the design of specific objectives that reflect the unique characteristics of multiple workflows. On the other hand, clustering-based approaches have garnered significant attention in the field of workflow scheduling over distributed computing resources due to their advantage in reducing data communication among tasks. Despite this, the effectiveness of clustering-based algorithms has not been extensively studied and validated in the context of multi-objective multi-workflow scheduling models. Motivated by these factors, we propose an approach for multiple workflows' multi-objective optimization (MOO), considering the new defined metric, fairness. We first mathematically formulate the fairness and define a fairness-involved MOO model. Then, we propose an advanced clustering-based resource optimization strategy in multiple workflow runs. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

Nature-Based Secondary Resource Recovery under Climate Change Uncertainty: A Robust Multi-Objective Optimisation Methodology

The management of high-volume (HV) waste poses a persistent challenge in sustainable materials management and represents an untapped opportunity in circular economy models. This study proposes a conceptual decision-making framework to operationalise a novel circular economy strategy for HV waste, involving temporary storage to facilitate nature-based secondary resource recovery. Using an illustrative case study of a candidate HV waste (legacy mining waste), we apply a robust multi-objective spatial optimisation approach at a national scale, employing an exact solution approach. Our methodology integrates mixed-integer linear programming to evaluate the economic viability, social benefits, and impacts of climate change uncertainties on nature-based solutions (NbS) implementation across diverse scenarios. The results demonstrate that NbS can enhance economic feasibility by incorporating carbon sequestration and employment benefits while demonstrating resilience against climate change projections to ensure long-term sustainability. The findings suggest that although NbS can improve the circular economy of HV nationally, it is essential to assess additional ecosystem services and address multiple uncertainties for effective macro-level sustainability assessment of HV management. This study offers a robust decision-making framework for policymakers and stakeholders to plan and implement nature-based circular economy strategies for HV waste streams at a national level while effectively managing long-term planning uncertainties.

A multi-objective optimization approach for integrating isomerization hydroprocessing unit with hydrogen network

A multi-objective optimization approach for integrating isomerization hydroprocessing unit with hydrogen network

Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning

Feature selection plays an essential role in data preprocessing, which can extract valuable information from extensive data, thereby enhancing the performance of machine learning classification. However, existing feature selection methods primarily focus on selecting feature subsets without considering the impact of classifier parameters on the optimal subset. Different from these works, this paper considers jointly optimizing the feature subset and classifier parameters to minimize the number of features and achieve a low classification error rate. Since feature selection is an optimization problem with binary solution space while classifier parameters involve both continuous and discrete variables, our formulated problem becomes a complex multi-objective mixed-variable problem. To address this challenge, we consider a single-objective optimization method and a multi-objective optimization approach. Specifically, in the single-objective optimization method, we adopt the linear weight method to convert our multiple objectives into a fitness function and then propose a mixed-variable grey wolf optimizer (MGWO) to optimize the function. The proposed MGWO introduces Chaos-Faure initialization, Log convergence factor adjustment, and optimal solution adaptive update operators to enhance its adaptability and balance the global and local search of the algorithm. Subsequently, an improved multi-objective grey wolf optimizer (IMOGWO) is introduced to directly address the problem. The proposed IMOGWO introduces improved initialization, local search, and binary variable mutation operators to balance its exploration and exploitation abilities, making it more suitable for our mixed-variable problem. Extensive simulation results show that our MGWO and IMOGWO outperform recent and classic baselines. Moreover, we also find that jointly optimizing classifier parameters can significantly improve classification accuracy.

A Novel Multi-Objective Dynamic Reliability Optimization Approach for a Planetary Gear Transmission Mechanism

Planetary gear transmission mechanisms (PGTMs) are widely used in mechanical transmission systems due to their compact structure and high transmission efficiency. To implement the reliability design and optimization of a PGTM, a novel multi-objective dynamic reliability optimization approach is proposed. First, a multi-objective reliability optimization model is established. Furthermore, considering the strength degradation of gears during service, a dynamic reliability analysis is conducted based on the theory of nonlinear fatigue damage accumulation. In addition, to improve computing efficiency, a random forest surrogate model based on the particle swarm optimization algorithm is proposed. Finally, an adaptive multi-objective evolutionary algorithm based on decomposition (AMOEA/D) is designed to optimize the mechanism, along with an adaptive neighborhood updating strategy and a hybrid crossover operator. The feasibility and superiority of the proposed approach are verified through an NGW planetary gear reducer. The results show that the proposed surrogate model can reduce the calculation cost and has high accuracy. The AMOEA/D algorithm can improve transmission efficiency, reduce gear volume and ensure reliability at the same time. It can provide guidance for actual gear production.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Topology optimization of smart structures to enhance the performances of vibration control and energy harvesting

Abstract With the growing interest in smart materials, the utilization of shunted piezoceramics for dynamic vibration control has gained significant attention due to their unique characteristics, such as the ability to absorb strain energy from vibrating systems and convert it into electrical energy. Designing and analyzing the behavior of structures in hybrid mitigation/harvesting conditions, considering both reliability and performance, pose challenges. This paper aims to achieve optimal design parameters for the structure by employing a multiobjective optimization approach that strikes a compromise between maximizing harvested power and minimizing structural damage. To evaluate the effectiveness of the design, topology optimization was conducted in three different cases to compare the results. By systematically exploring the design space, these cases provide insights into the influence of various parameters on the structural performance. In addition, to enhance computational efficiency, the structure was represented as a metamodel using neural networks. This approach enables rapid evaluation and prediction of the structure’s behavior, facilitating the optimization process. By integrating multiobjective optimization, topology optimization, and metamodeling techniques, this study aims to provide valuable insights into the optimal design of structures that simultaneously incorporate shunt circuitry for vibration control and energy harvesting, leading to improved performance and reliability.

Spatial multi-objective optimization towards low-carbon transition in the Yangtze River Economic Belt of China

ContextIn the context of urbanization and climate change, optimizing land use patterns is fundamental for sustainable regional development and low-carbon transition. However, achieving sustainable land use patterns that support a low-carbon transition while maintaining economic prosperity remains challenging.ObjectivesOur goals are to develop a model for low-carbon transition and explore strategies for optimizing land use given future land use constraints.MethodsWe propose a multi-objective optimization approach to explore land use transitions towards low carbon by considering emissions reduction, ecological protection, and economic development, taking Yangtze River Economic Belt of China was selected as a case study area for empirical research.ResultsOur research on Yangtze River Economic Belt of China from 2000 to 2020 reveals that the refined land use strategies, aiming at achieving a low-carbon transition by 2035, have the capacity to substantially reduce carbon emissions. Simultaneously, these strategies ensure the preservation of a higher ecosystem service value and enhance economic advantages. Our multi-objective optimized land use patterns reduce the emission growth by nearly 5% compared to the baseline. Meanwhile, our multi-objective optimized land use patterns demonstrate a nearly 7% growth rate in economic benefits and the ecosystem service value also shows slight improvement. Recognizing regional variations in sustainable land use patterns, we recommend customized local guidance towards aligning with these optimal outcomes.ConclusionOur study offers valuable policy insights for regional land use to facilitate a low-carbon transition, which can contribute to navigating the trade-offs between carbon reduction, ecological protection, and economic prosperity.

Genetic algorithm-based optimization of combined supercritical CO2 power and flash-tank enhanced transcritical CO2 refrigeration cycle for shipboard waste heat recuperation

The shipping industry significantly contributes to global trade and economy, but is also responsible for approximately ∼3 % of global greenhouse gas (GHG) emissions. Harnessing waste heat from marine vessels to perform useful work is a potential solution to reduce these emissions. Although the Organic Rankine Cycle (ORC) and CO2 cycles have been studied separately for waste heat recovery, a combined power and refrigeration systems, utilizing both cycles have not been reported. Herein, a combined system, “Marine Energy Recovery System (MERS)”, that integrates the supercritical CO2 Brayton power cycle with a flash-tank-enhanced transcritical CO2 refrigeration cycle, is proposed, aiming to generate power and provide cooling simultaneously. The power cycle incorporates an ORC, and a flash-tank is introduced within the refrigeration cycle to improve system performance. Both cycles share a low-temperature recuperator and a gas cooler to further utilize the heat between them. The MERS is evaluated from both a 1st and 2nd law perspective against various performance metrics under different operating conditions, such as: gas cooler pressure, evaporation temperature, and turbine inlet temperature and pressure. Results elucidated that the MERS achieved energy and exergy efficiencies of 54.62 % and 54.90 %, respectively, representing significant improvements over similar systems. The net work output improved by 717.55 kW and the net cooling capacity by 515.7 kW relative to the reference system. Furthermore, a COP of 2.99 outlines its potential as a cooling system. To further enhance MERS’s performance, a multi-objective optimization approach is employed, utilizing Artificial Neural Network (ANN) and Genetic Algorithm (GA). Optimal operational conditions yielded an energy efficiency of 74.95 % while maintaining a net work output of 6890.55 kW with gas cooler pressure, turbine inlet temperature, and gas cooler outlet temperature being 9.5 MPa, 461.76°C and 50°C respectively. These findings support the reliability and improved design of the MERS for future marine applications.

Selective multiple kernel fuzzy clustering with locality preserved ensemble

Multiple kernel fuzzy clustering (MKFC) has demonstrated promising performance in capturing the non-linear relationships within data. However, its effectiveness relies heavily on the appropriate selection of the fuzzification coefficient and kernel functions. To address this challenge, this paper proposes a novel clustering ensemble approach for improving the robustness and accuracy of the MKFC algorithm. The proposed method employs a multi-objective evolutionary optimization approach to heuristically select the optimal fuzzification and kernel coefficients. By employing the selected coefficients, the MKFC algorithm generates a diverse set of accurate candidate clustering results. Subsequently, a locality preserved ensemble mechanism is introduced to derive the final partition matrix, ensuring that all candidate clusterings within the Pareto non-dominated set contribute to the final consensus matrix. Moreover, this mechanism incorporates the knowledge about the locality of the dataset via a graph regularization term, thereby further enhancing the clustering performance. Comprehensive experiments conducted on widely adopted benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.

A multi-objective optimization approach for harnessing rainwater in changing climate

As the world grapples with the profound impacts of climate change, water scarcity has become a pressing issue. However, there is a shortage of in-depth research on the trade-offs between water resource dependence and the economic, ecological, and social needs of arid and semi-arid regions like Lanzhou, China. Flower cultivation in Lanzhou relies heavily on the Yellow River, often overlooking the potential of natural rainfall. Here we first calibrated a water balance model through artificial precipitation experiments in a Soil and Water Conservation Demonstration Park in Lanzhou. We then developed a multi-objective optimization model to balance the cost-benefit considerations of various plausible measures across economic, ecological, and social dimensions in the searching for solutions that are more adaptable to climate change and local development needs. Model simulations show that the solutions we designed can effectively manage water-shortage days, significantly reduce Yellow River water extraction, and improve cost-effectiveness, meeting 66%–80% of water needs for flower cultivation in the studied park. The findings highlight the potential of rainwater collection and utilization solutions to mitigate water scarcity in arid and semi-arid cities, thereby enriching water resource management.

Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys

Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements. This study introduces a multi-objective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading. The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus, a low thermal expansion, a high heat capacity, and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading. Contrasting two optimization procedures, we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship. Conversely, the Pareto front is much broader in the performance space when these properties have antagonistic relationships. Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

A multi-objective planning tool for the optimal supply of green hydrogen for an industrial port area decarbonization

This study addresses the challenge of decarbonizing highly energy-intensive Industrial Port Areas (IPA), focusing on emissions from various sources like ship traffic, warehouses, buildings, cargo handling equipment and hard-to-abate industry, typically hosted in port areas. The analysis and proposal of technological solutions and their optimal integration in the context of IPA is a topic of growing scientific interest with considerable social and economic implications. Representing the main novelties of the work, this study introduces (i) the development of a novel IPA energy and green hydrogen hub located in a tropical region (Singapore); (ii) a multi-objective optimization approach to analyse, synthesize and optimize the design and operation of the hydrogen and energy hub, with the aim of supporting decision-making for decarbonization investments. A sensitivity analysis identifies key parameters affecting optimization results, indicating that for large hydrogen demands, imported ammonia economically outperforms other green hydrogen carriers. Conversely, local hydrogen production via electrolysis becomes economically viable when the capital cost of alkaline electrolyser drops by at least 30 %. Carbon tax influences the choice of green hydrogen, but its price variation mainly impacts system operation rather than design. Fuel cells and batteries are not considered economically feasible solutions in any scenario.

A stretchable and self-powered strain sensor with elastomeric electret

Stretchable deformation sensors play an important role in the perception and autonomy of soft robots. While various sensors have been reported, the mechanical sensor with self-powered capability and stretchability to suit extreme environment is urgently required. In this study, a stretchable, self-powered, sensitivity adjustable, and long-term stability strain sensor based on the stretchable electret is researched for deformation monitoring. This stretchable and self-powered sensor is composed of the dielectric elastomer with designed elastic modulus gradient and the stretchable electret with elastomeric and Nano-particles. The electro-mechanical capability of this designed sensor is researched and optimized by multi-objective optimization approach. The sensor exhibits self-powered capability, stretchability (tensile strain from 0% to 20%), tunable sensitivity (optimized from 5.7 to 5.9 pC mm−1), and stability, where electromechanical performance remains stable after 8000 stretching cycles. The feasibility of its autonomous sensing which promotes a wide range of potential application in soft robotics is demonstrated. This elastic modulus gradient-induced and net charge design significantly broadened the potential applications with large deformation and low-attached stiffness.

The Case for Hybrid Multi-Objective Optimisation in High-Stakes Machine Learning Applications

Most classification (supervised learning) algorithms optimise a single objective, typically the predictive performance of the learned classification model. However, in high-stake classification applications, involving e.g. decisions about whether or not an individual should undergo a medical surgery, be granted a loan or be hired for a job, often there is a need to optimise multiple objectives, such as the predictive performance, interpretability or fairness of the learned model. In this context, this position paper discusses the pros and cons of two different multi-objective optimisation approaches (the Pareto and the lexicographic approaches), and proposes a conceptual framework for hybrid multi-objective optimisation, combining those two approaches.

A multi-objective optimization approach for overlapping dynamic community detection

A multi-objective optimization approach for overlapping dynamic community detection

A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking.

Multi objective optimization and evaluation approach of prefabricated component combination solutions using NSGA-II and simulated annealing optimized projection pursuit method

As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions.

Enhancing brushless DC motor wheel design using single and multi-objective heat transfer search optimization approach

Enhancing brushless DC motor wheel design using single and multi-objective heat transfer search optimization approach