Multi-objective Approach Research Articles

Artificial Neural Network Approach for Analysis and Prediction of Solar Radiation and Wind Speed in Telecommunication Systems

The use of renewable energy sources in the power grid is essential for maintaining economic growth, especially for many African countries. Therefore, these countries can also install advanced artificial intelligence technology in their airport’s power system to capture renewable energy. The paper aims to propose a real-time power management algorithm for a distributed hybrid renewable energy grid. It continues with the network modeling with the integration of solar and wind power. A multi-objective approach is proposed for the maximization of the spatial spectral efficiency and the energy efficiency as a function of the number of wind turbines and solar photovoltaic panels. Radio parameters for a Mobile Wireless inter-operability Medium Access (WiMAX) technology are considered for the simulation in MATLAB software. The results are quite mitigated but theoretically promising for the integration of green energy into the current telecommunication systems

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

Optimizing green solid transportation with carbon cap and trade: a multi-objective two-stage approach in a type-2 Pythagorean fuzzy context

Optimizing green solid transportation with carbon cap and trade: a multi-objective two-stage approach in a type-2 Pythagorean fuzzy context

Performance Analysis and Optimization of Supercritical CO2 Recompression Brayton Cycle Coupled With Organic Flash Cycle With a Two-Phase Expander

Abstract In this work, a combined supercritical CO2 recompression Brayton cycle (SCRBC)/organic flash cycle with a two-phase expander (OFCT) system is proposed to improve the thermal efficiency of the SCRBC, which utilizes a two-phase expander to replace the high-pressure throttling valve of a basic organic flash cycle (OFC). In addition, the OFCT is coupled at the waste heat end of the SCRBC as the bottom cycle for the use of waste heat at low temperatures. A comprehensive comparison is carried out for different organic working fluids, including the R123, R245fa, R142B, R236ea, and R600, regarding the thermal performance, environmental effect, and safety levels. Furthermore, influences of various factors on the thermal performance of the combined SCRBC/OFCT cycle are also examined, including the top cycle pressure ratio, top cycle turbine inlet temperature, mass flowrate ratio, evaporation temperature, and the condenser's pinch point temperature difference. A multi-objective optimization approach is employed on the combined SCRBC/OFCT system, which considers both the thermal efficiency and the specific investment cost as the objective function, and the optimization procedure is implemented through the nondominated sorting genetic algorithm II (NSGA-II) algorithm. The Pareto solution set and the compromise solution are finally obtained. The results indicate that the optimized combined SCRBC/OFCT system can improve the thermal efficiency by 11.75% and 9.70% when compared with the SCRBC and SCRBC/OFC, respectively.

Structural optimization and heat-fluid-solid simulation of a graded corundum-calcium hexaluminate purging plug by a multi-objective genetic algorithm approach

The purging plug is essential for enhancing production efficiency in molten steel refining, yet it faces challenges related to structural integrity due to its lifespan often not aligning with the ladle's repair cycle. This study introduces a novel corundum-calcium hexaluminate purging plug with gradual holes designed to alleviate internal stress concentration. Utilizing a multi-objective optimization model and fluid-solid coupling heat transfer method, the impact of structural parameters on thermal and mechanical properties was systematically investigated. The numerical simulation results indicate that increasing the number of gradient layers positively impacts temperature distribution. Notably, the D-210 (1.0 mm diameter) aperture reduces the stress gradient Δσmax by 648.06 MPa compared to D-212 (1.2 mm diameter) at the Y = 0.313 m section. Additionally, variation in inclination angle impacts tensile stress σt and shear stress τ, an inclination angle of 6° (α6) reduces maximum tensile stress by 211.41 MPa compared to an angle of 0° (α0).

Multiobjective enterprise development algorithm for optimizing structural design by weight and displacement

This study presents a novel metaheuristic optimization algorithm for complex multiobjective engineering problems. By integrating advanced population and nondominated sorting techniques into the existing single-objective enterprise development algorithm, this new multiobjective approach effectively identifies Pareto-optimal solutions. The algorithm leverages these techniques to explore engineering solutions within multiobjective search spaces. We evaluated its performance using 29 multiobjective mathematical benchmark problems and conducted a comparative analysis against ten established metaheuristic optimization algorithms. The results demonstrate that the algorithm produces highly accurate approximations of Pareto-optimal fronts while maintaining a uniform distribution of solutions. Additionally, the algorithm was applied to real-world engineering challenges, including the optimization of structures such as the 942-bar tower, the 1536-bar double-layer barrel vault, and the 1410-bar double-layer dome truss, with the primary objectives of minimizing both structural weight and maximum nodal deflection. The findings highlight the algorithm's effectiveness in solving practical engineering problems and consistently achieving optimal Pareto fronts.

Safety-driven design of carbon capture utilization and storage (CCUS) supply chains: A multi-objective optimization approach

Carbon capture, utilization, and storage supply chains (CCUS) play a pivotal role in achieving sustainability targets but necessitate meticulous risk identification and mitigation measures. Traditional safety assessments often occur post-design, constraining proactive risk management efforts. Hence, there is a pressing need to optimize safety performance during the design stages. To address this challenge, a framework for evaluating and optimizing CCUS supply chain safety performance using inherent safety index system (ISI) is introduced. Recognizing the trade-offs between total cost, environmental impact reduction, and risk mitigation, our approach considers multi-objective optimization to concurrently address these sustainability objectives and generate a Pareto set of solutions. Utilizing the augmented ε-constraint method, we applied this framework to optimize CCUS networks and develop sustainable designs across three key objectives. The method was applied to a CCUS system that includes various CO2 utilization pathways to minimize the total annual cost, CO2 emissions, and safety risks. The resulting Pareto surface illustrates unique network configurations, each representing a distinct trade-off scenario. Through a case study, we optimized a CCUS network to achieve economic, environmental, and safety objectives. The most economically viable design, with a total annual cost of $97 million and a 40 % net carbon reduction, prioritizes CO2 utilization for value-added products, while limiting CO2 sequestration. Conversely, safety-focused designs shift utilization towards safer routes, including CO2 sequestration and algae production. The proposed framework offers a systematic approach to developing sustainable CCUS supply chain designs, balancing economic viability, environmental sustainability, and safety.

Optimization strategies for enhancing diesel engine performance and emissions control with biofuel blends: A multi-objective approach

This study examines the effects of engine compression ratio, load, and biofuel composition on performance, combustion, and emissions. The experimental setup includes testing different compression ratios (16, 17, 18) and loads (25 %, 50 %, 75 % torque) at a fixed speed of 1500 rpm, using Thai standard diesel as the control fuel. Biofuel variations tested include neat biodiesel, neat biokerosene, and blends of 30 % biokerosene with 70 % biodiesel and 50 % biokerosene with 50 % biodiesel. The results show that an increased compression ratio enhances combustion efficiency, reduces brake specific fuel consumption, and improves brake thermal efficiency, although it also leads to higher nitrogen oxide emissions. Biofuels improve brake thermal efficiency due to their higher oxygen content. However, the increased viscosity of biodiesel can hinder fuel atomization, resulting in higher emissions. In contrast, biokerosene improves brake thermal efficiency by facilitating earlier combustion and reducing emissions. Performance is primarily affected by load, while emissions are influenced by both biokerosene content and load. Gradient boosting models and optimization techniques, such as the non-dominated sorting genetic algorithm III (NSGA-III) and adaptive geometry estimation-based multi-objective evolutionary algorithms (AGE-MOEA), produce consistent results, with AGE-MOEA identifying denser optimal points. The findings suggest that optimal biokerosene blends ranging from 20 % to 40 %, across all compression ratios and loads of 50 %–75 %, provide valuable insights for advancing sustainable fuel utilization practices in automotive vehicles.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data. To reduce the large solution space caused by high dimensionality, we incorporate the recursive feature elimination strategy based on mining the archive of evolved GP solutions. A progressive domination Pareto archive evolution strategy (PD-PAES), which optimizes the objectives in a specific order according to their objectives, is proposed to evaluate the GP individuals and maintain a better diversity of solutions. Besides, to address the seriously imbalanced class issue caused by traditional binary decomposition (BD) one versus rest (OVR) for multiclass classification problems, we design a method named BD with a similar positive and negative class size (BD-SPNCS) to generate a set of auxiliary classifiers. Experimental results on benchmark and real-world datasets demonstrate that our proposed PS-MOGP outperforms state-of-the-art traditional and evolutionary classification methods in the context of high-dimensional data classification.

Multiobjective optimization of collaborative robotic task sequence assignment problems under collision-free constraints

This paper proposes a multiobjective optimization approach to address the challenge of collaborative manufacturing with multiple robot arms. Given the necessity for multiple robot arms to work together, the potential for collisions between robotic motions is a significant concern, and the automated task sequence assignment for robots becomes increasingly complex. Previous research has either simplified the collision-free conditions in a limited working area, or employed a master-slave approach to obtain only a local solution. Consequently, we propose a unified global optimization approach for simultaneously addressing various collaborative manufacturing issues, including robotic task sequence assignment (RTSA), multiple inverse kinematics (IK) selection, joint-space collision-free operations and multiple manufacturing objectives. As the optimal collaborative RTSA problem is a combinatorial optimization problem with non-deterministic polynomial-time hard (NP-hard) complexity, this paper presents a hybrid nondominated sorting genetic algorithm III (NSGA-III) method that integrates a Hamming-distance-based method and a greedy strategy within NSGA-III to improve population diversity and solution quality. To validate the efficacy of the proposed approach, simulation experiments were conducted on cooperative manufacturing scenarios, with two objectives: task completion time and task load balancing. The experimental results demonstrate that the proposed approach is effective in obtaining collision-free Pareto solutions. Furthermore, the proposed hybrid NSGA-III method obtains superior solutions compared to the original method in the studied problem, as measured by two performance indices.

Optimizing sustainable development in arid river basins: A multi-objective approach to balancing water, energy, economy, carbon and ecology nexus

The ongoing water crisis poses significant threats to the socioeconomic sustainability and ecological security of arid and semi-arid river basins. Achieving Sustainable Development Goals (SDGs) within a complex socio-ecological nexus requires effective and balanced resource management. However, due to the intricate interactions between human societies and environmental systems, the tradeoffs and synergies of different SDGs remain unclear, posing a substantial challenge for collaborative management of natural resources. Here we introduce a gray fractional multi-objective optimization (GFMOP) model to balance multi-dimensional SDGs through a novel water–energy–economy–carbon–ecology nexus perspective. The model was applied to a typical arid river basin in Northwest China, where thirty-two scenarios were explored, considering factors such as shared socioeconomic pathways, carbon removal rates, water conveyance efficiencies, and ecological requirements. The results reveal a strong tradeoff between marginal benefit and carbon emission intensity, indicating that improving the economic efficiency of water use can simultaneously reduce emissions and protect the environment. Given the immense power generation potential, wind power development should be prioritized in the future, with its share in the energy structure projected to increase to 23.3% by 2060. Furthermore, promoting carbon capture technologies and expanding grassland coverage are recommended to achieve regional carbon neutrality, contributing 39.5% and 49.1% to carbon absorption during 2021–2060, respectively. Compared with traditional single-objective models, GFMOP demonstrates a superiority in uncovering interrelationships among multiple SDGs and identifying compromised alternatives within the compound socio-ecological nexus. The model also provides detailed strategies for resource allocation and pollutant control, offering valuable guidance to policymakers and stakeholders in pursuing sustainable and harmonious watershed management.

The multi-objective data-driven approach: A route to drive performance optimization in the food industry

Although standardized, food processing is subject to many sources of variability resulting from compositional and structural variabilities of raw materials and/or ingredients, human perception and intervention in the process, capabilities of processing tools and their wear and tear, etc. Altogether, they affect the reproducibility of final product characteristics representing deviations to standard, the production yield impacting the economic performance of the food manufacturing process, and many other performance indicators. They are grossly classified as economic, quality and environmental indicators and their simultaneous consideration can be used to define the overall performance of a manufacturing process. Optimizing the overall performance of food processing requires the use of multi-objective optimization methods. Multi-objective optimization methods include five steps: defining the objectives, modelling performance indicators, formulating the problem and constraints, solving the multi-objective problem, and finally identifying an ideal solution. The integration of data-driven approach, particularly machine learning, into the multi-objective optimization offers new perspectives for optimizing and controlling food processes. The potential of this approach is still underestimated by the food industry sector.

Exploring cryo-MQL medium for hard machining of hastelloy C276: a multi-objective optimization approach

Exploring cryo-MQL medium for hard machining of hastelloy C276: a multi-objective optimization approach

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models

A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-end for e-bike riders by encompassing a broader range of test scenarios. Initially, large-scale rider injury response data were collected using automated Madymo simulations. A machine learning model was then trained to accurately predict the risk of rider injury under varied crash conditions. Subsequently, this model was integrated into a multi-objective optimization framework, combined with multi-criteria decision analysis, to effectively evaluate and rank various design alternatives on the Pareto frontier. This process entailed a comparative analysis of the design in a baseline scenario before and after optimization, focusing on both kinematic and injury responses of riders. Through detailed injury mechanism analysis, key design variables such as the height of the hood front and the width of the bumper were identified. This led to the proposal of specific optimization strategies for these structural parameters. The results from this study demonstrate that the proposed optimization method not only guides the design process accurately and efficiently but also balances the injury risks across different body parts. This approach significantly reduces the injury risk for riders in car-to-e-bike collisions and provides actionable insights for vehicle design enhancements.

An indicator-based multi-objective variable neighborhood search approach for query-focused summarization

Currently, automatic multi-document summarization is an interesting subject in numerous fields of study. As a part of it, query-focused summarization is becoming increasingly important in recent times. These methods can automatically produce a summary based on a query given by the user, including the most relevant information from the query at the same time as the redundancy among sentences is reduced. This can be achieved by developing and applying a multi-objective optimization approach. In this paper, an Indicator-based Multi-Objective Variable Neighborhood Search (IMOVNS) algorithm has been designed, implemented, and tested for the query-focused extractive multi-document summarization problem. Experiments have been carried out with datasets from Text Analysis Conference (TAC). The results were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. IMOVNS has greatly improved the results presented in the scientific literature, providing improvement percentages in ROUGE metric reaching up to 69.24% in ROUGE-1, up to 57.70% in ROUGE-2, and up to 77.37% in ROUGE-SU4 scores. Hence, the proposed IMOVNS offers a promising solution to the query-focused summarization problem, thus highlighting its efficacy and potential for enhancing automatic summarization techniques.

Semi-supervised accuracy predictor-based multi-objective neural architecture search

The rise of neural architecture search (NAS) demonstrates the deep exploration between the neural network architecture and its performance (e.g., accuracy). Many NAS methods are inefficient because they train all candidates from scratch to obtain their accuracies. Although predictor-based NAS algorithms have been vigorously developed to efficiently and accurately evaluate the performance of candidate architectures, the training of accuracy predictors still require hundreds of architectures with ground truth. To overcome this shortcoming, this paper investigates an evolutionary-based NAS method, which constructs a semi-supervised accuracy predictor to efficiently and accurately evaluate candidate architectures. A one-time extractor and strong regressors are implemented to further enhance the prediction performance of the semi-supervised accuracy predictor. Furthermore, a multi-objective approach is developed to find architectures with high ground truth in a tradeoff between high prediction accuracy and prediction confidence. Experimental results demonstrate the strong competitiveness of the proposed approach on NAS benchmarks. The code is available at https://github.com/outofstyle/SAPMNAS.

Probabilistic prediction-based multi-objective optimization approach for multi-energy virtual power plant

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Multiobjective sparse unmixing based hyperspectral change detection

Hyperspectral unmixing can provide the composition of ground objects, while change detection can identify the changes of the same region over time. Therefore, unmixing based hyperspectral change detection can investigate changes in a subpixel-level. It can provide not only whether the change happens or not but also how to change. This paper will gradually establish an unconstrained sparse unmixing based change detection model and design a multiobjective optimization sparse unmixing approach (termed MoSU-CD) to solve it for more subpixel-level change details. Compared with the existing sparse unmixing based change detection approaches, MoSU-CD directly unmixes the difference map of multi-temporal images rather than multi-temporal images themselves respectively. Then it explores the change details of one substance in each pixel based on the unmixing results, i.e., endmembers and abundances. In addition, in order to improve the change detection accuracy, an ensemble decision strategy is designed for the Pareto optimal solutions obtained by multiobjective optimization method. Based on this strategy, the subpixel-level changes can be aggregated to generate an aggressive pixel-level change map. The experimental results on synthetic and real data sets demonstrate that the proposed MoSU-CD not only outperforms the benchmark and state-of-art approaches in terms of pixel-level change detection but also provides additional subpixel-level change information.