Multi-objective Optimization Algorithm Research Articles

Collaborative Optimization of Direct Current Distribution Network Based on Scaled Electric Vehicles Charging and Discharging and Soft Open Points Topology Reconfiguration

In order to reduce the impact of the performance degradation of a direct current (DC) distribution network system caused by the access of scaled electric vehicles (EVs), this paper proposes a collaborative optimization method for a DC distribution network based on scaled EVs charging and discharging and soft open points (SOPs) topology reconfiguration. Firstly, based on the normal charging of scaled EVs, the EV discharge power model and the discharge response model were established based on the V2G (vehicle-to-grid) characteristic. Based on the characteristics of SOPs regulating voltage and power distribution, the SOP model and its equivalent model of DC system are established to identify the collaborative optimization of scaled EVs charging and discharging and SOPs topology reconstruction. Secondly, the bi-level model that optimizes multi-objects, including distribution network system loss, total voltage deviation and customer benefits, is established. The upper and lower models use the multi-objective particle swarm optimization (MOPSO) algorithm and simulated annealing algorithm to jointly optimize the optimal EV discharge power and the optimal SOP access configuration simultaneously. Finally, the effectiveness of the proposed collaborative optimization method is verified by a modified IEEE 33-node DC system example.

Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

ABSTRACTUnderwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross‐domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region‐Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real‐world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments.

Numerical Modeling and Structure Optimization for Magnetic Levitation Planar Machine Using PCB Coils

Magnetically levitated (ML) systems that incorporate PCB coils represent a growing trend in precision machining, valued for their controllable current flow and high fill factor. The size of modern power devices is decreasing to enhance power density, minimize parasitic inductance, and reduce power losses. However, due to the high resistance of PCB coils, managing heat generation has become a significant area of study. This paper seeks to optimize PCB coil design to minimize power loss and control peak temperatures in ML systems, using a numerical model. An improved magnetic node model is employed to construct the magnetic fields of an ML system. The proposed optimization method considers the interdependencies among parameters to reduce overall power loss from coil resistance and switching losses in the H-bridge circuit, while enhancing heat dissipation efficiency in steady-state operation. A heuristic multi-objective optimization algorithm is employed to optimize the design of the ML actuator. The optimization process initially focuses on the PCB coils, with the magnet size held constant. Once the optimal coil parameters are identified, the magnet volume is optimized. By integrating a theoretical analysis with simulation, this approach effectively addresses the optimization challenges and achieves the desired performance for the ML actuator. Coils and magnets are constructed based on the optimized design and tested by the magnetic field simulation software Radia, confirming the feasibility of the approach. The method was also applied to a different type of ML system for comparison, demonstrating the universality of the proposed strategy. In this optimization effort, the maximum temperature reduction reached an impressive 50 °C

A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a Multi-objective Combinatorial Optimization in Multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary Immune Algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

Automated Volumetric Milling Area Planning for Acoustic Neuroma Surgery via Evolutionary Multi-Objective Optimization

Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g., blood vessels and nerves) complicate this task. In order to determine the bone area to mill using preoperative CT images automatically, we propose an automated planning method using evolutionary multi-objective optimization for safer and more efficient milling plans. High-resolution segmentation of the adjacent risk structures is performed on preoperative CT images with a template-based approach. The maximum milling area is defined based on constraints from the risk structures and tool dimensions. Deformation fields are used to simplify the volumetric area into limited continuous parameters suitable for optimization. Finally, a multi-objective optimization algorithm is used to achieve a Pareto-optimal design. Compared with manual planning on six volumes, our method reduced the potential damage to the scala vestibuli by 29.8%, improved the milling boundary smoothness by 78.3%, and increased target accessibility by 26.4%. Assessment by surgeons confirmed the clinical feasibility of the generated plans. In summary, this study presents a parameterization approach to irregular volumetric regions, enabling automated milling area planning through optimization techniques that ensure safety and feasibility. This method is also adaptable to various volumetric planning scenarios.

Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation

Abstract Bayesian optimisation (BO) protocols grounded in active learning (AL) principles have gained significant recognition for their ability to efficiently optimize black-box objective functions. This capability is critical for advancing autonomous and high-throughput materials design and discovery processes. However, the application of these protocols in materials science, particularly in the design of novel alloys with multiple targeted properties, remains constrained by computational complexity and the absence of reliable and robust acquisition functions for multiobjective optimisation. Recent advancements have demonstrated that expected hypervolume-based geometrical acquisition functions outperform other multiobjective optimisation algorithms, such as Thompson Sampling Efficient Multiobjective optimisation and pareto efficient global optimisation (parEGO), in both performance and speed. This study evaluates several leading multiobjective BO acquisition functions–namely, parallel expected hypervolume improvement (qEHVI), noisy qEHVI, parallel parEGO, and parallel noisy parEGO (qNparEGO)–in optimizing the physical properties of multi-component alloys. Our findings highlight the superior performance of the qEHVI acquisition function in identifying the optimal Pareto front across 1-, 2-, and 3-objective aluminum alloy optimisation problems, all within a constrained evaluation budget and reasonable computational cost. Furthermore, we explore the impact of various surrogate model optimisation methods from both computational cost and efficiency perspectives. Finally, we demonstrate the effectiveness of a pool-based AL protocol in expediting the discovery process by executing multiple computational and experimental campaigns in each iteration. This approach is particularly advantageous for deployment in massively parallel high-throughput synthesis facilities and advanced computing architectures.

Online Physical Education Teaching Resources Integration Method Based on Multi-Objective Optimization Algorithm

In order to improve the sharing level of online physical education teaching resources, the integration design of online physical education teaching resources is carried out by using MOOC resource information fusion technology and closed frequent item fusion calculation method, and the intelligent scheduling and management ability of online physical education teaching resources is improved. An online physical education teaching resource integration model based on big data information fusion and cluster scheduling is proposed. The factors of online physical education teaching demand scale are initialized, the structure of common factors of online physical education teaching resources is determined by factor analysis method, the orthogonal rotation of indicators by maximum variance method is adopted, the cloud computing model is adopted to analyze the integration structure of online physical education teaching resources, and the feature quantity of cross frequent items rules of online physical education teaching resources is extracted. The parallel K-means clustering model of online physical education resources integration is constructed, the fuzzy correlation degree features of online physical education resources are calculated, the extracted correlation features of online physical education resources are processed by C-means clustering method for big data fusion, the multi-objective optimization technology is adopted for online physical education resources integration and adaptive scheduling, and the adaptive scheduling ability of online physical education resources is improved. The SPSS statistical software is used for empirical analysis, and the results show that the integration degree of online physical education teaching resources by this method is high, which promotes the development of online physical education teaching.

Dynamic coupling of qualitative–quantitative models for operation of water resources based on environmental criteria

Concerning issues include the distribution of scarce water resources, the quality of utilized water, environmental repercussions, and regulations for the sustainable use of water resources. In the management of water resources, optimal qualitative–quantitative exploitation of surface water bodies is regarded as a desirable strategy. The Dez River surface water system from the Dez regulatory dam to Band-e-Ghir is selected in the current paper to create a qualitative–quantitative model that can determine the best exploitation strategies. A dynamic linkage between qualitative and quantitative models is built in order to simulate the current exploitation conditions under the umbrella of the best-case scenario. In this coupled system, hydraulic relationships are established between all of the system’s components. The available data are shared between two models in this structure to simulate the qualitative and quantitative effects of surface water. Then, a new structure is produced to derive the best policies for exploiting the dam and the river by connecting the multi-objective particle swarm optimization algorithm with the qualitative–quantitative coupled model body. The monthly river environmental demand is one of the decision variables in the ideal scenario, and the goals include boosting the percentage of supply demands and minimizing the violation of quality standards. The best-case scenario’s implementation increases the likelihood that all plain demands will be met, regardless of priority. Furthermore, in comparison with the reference scenario, the results of the optimal scenario show that not only are the concentrations of contaminants and qualitative parameters increased, but there are also only minimal violations of the quality and pollution standards of the river water in the majority of river points, particularly in the locations of agricultural withdrawals. The findings demonstrate that using the qualitative–quantitative dynamic relationship between water resources and the development of the coupled model using the NSGA-II algorithm allows us to better plan for the appropriate use of existing water resources by taking into account all stakeholders in such a way that, in addition to meeting needs, maintains the river quality close to standard limits throughout the exploitation period. By using this strategy, users will be informed of the negative effects of their actions, as well as the encroachment on river boundaries and associated consequences.

Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency

Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency

An optimization method for terahertz metamaterial absorber based on MOPSO

Metamaterials can freely control terahertz waves to obtain the desired electromagnetic characteristics by designing the geometry and direction of the unit structure, which is widely used in sensing, communication and stealth technology in radar. The traditional design of terahertz metamaterial absorber usually requires continuous structural adjustment and a large number of simulations to meet the expected requirements. The process is heavily dependent on the experience of researchers, and the physical modeling and simulation solution process is time-consuming and inefficient, which has greatly hindered the development of metamaterial absorbers. Therefore, deep learning has been used to predict the structural parameters or spectra of metamaterial absorbers due to its powerful learning ability. However, when designing a new structure, a large number of training samples need to be reprepared, which is time-consuming and not universal. Particle swarm optimization can quickly converge to the optimal solution through the sharing and cooperation of individual information in the group, without prior preparation. Therefore, this paper proposed a fast design method of terahertz metamaterial absorber based on multi-objective particle swarm optimization algorithm. Taking a new center symmetric absorber structure composed of four L as an example, the structure parameters are optimized to achieve fast automatic design of metamaterial absorber. The multi-objective particle swarm optimization takes the absorptivity and quality factor as independent targets to design the structure parameters of the absorber, realizes the dual-objective optimization of the absorber, and overcomes the shortcoming of the multi-objective conflict that PSO cannot solve. The optimally-designed absorber achieves perfect absorption at 1.613THz with a quality factor of up to 319.72 and a sensing sensitivity of 264.5GHz/RIU when used for refractive index sensing. In addition, the causes of absorption peaks are analyzed in detail using impedance matching, surface current, and electric field distribution. By studying the polarization characteristics of the absorber, it is found that it is not sensitive to polarization, which is more stable in practical application. In summary, the multi-objective particle swarm optimization algorithm can realize the design according to the demand, reduce the experience requirement of researchers in the design of metamaterial absorber, improve the design efficiency and performance, and have great application potential in the design of terahertz functional devices.

Optimization of fatigue life of the seismic vibrator baseplate considering the coupling effect of welding residual stress

The seismic vibrator baseplate (SVB) is a welded structure. Ignoring welding residual stress (WRS) and only considering cyclic stress can decrease the accuracy of fatigue life assessments. This study aims to investigate the fatigue life prediction and optimization of SVB under the influence of WRS. A welding model of SVB and an SVB-ground excitation model were developed, and the stress distribution of SVB was determined using the finite element method. A fatigue life prediction study that incorporates WRS, based on a modified S-N curve, was conducted. A multi-objective fatigue life optimization analysis was performed using genetic algorithms. The results shows that the backing weld (BW) of the SVB is the most susceptible area to fatigue failure. Under the combined effects of WRS and working stress, the fatigue life of the SVB is 8.14 years. When considering WRS, the prediction results align well with field data, showing an error margin of less than 5 %. WRS is identified as a critical factor influencing the fatigue life of SVB, and optimizing welding parameters can effectively reduce WRS, thus extending the service life. The optimal welding parameters were found to be welding interpass temperature of 105 °C and welding speed of 10.23 mm/s. After optimization, the fatigue life of the SVB increased to 10.23 years, a 25.68 % improvement over the pre-optimization condition. Previous studies did not consider the influence of WRS, whereas this work integrates it into the fatigue analysis, producing a modified S-N curve that enhances the accuracy of life prediction. Furthermore, multi-objective optimization algorithms and fusion decision-making methods were applied to the fatigue life optimization of the SVB, offering novel research approaches and methodologies. This study provides theoretical guidance for the structural design and optimization of the SVB, aligning with practical engineering needs and showing substantial application value.

A new multi-objective optimization algorithm for separation processes

This paper proposes a new algorithm, named guided population adaptive genetic algorithm (GAGA), to solve optimization problem of the complex separation processes. In GAGA, the strategies of adaptive crossover and mutation, leader selection, boundary random walk, neighborhood guidance and spiral updating position are introduced to enhance the guidance of population evolution. The capability of GAGA is investigated by 8 multi-objective benchmark problems. The results are compared with four well-known multi-objective optimization algorithms. The average values of inverted generational distance (IGD) and generational distance (GD) are 0.1626 and 0.5363, respectively, which is proved to be a robust and reliable model. Moreover, GAGA is validated through methacrylic acid (MAA) separation process with multi-cycle flows. The optimization efficiency of GAGA has accelerated by 2 times compared with non-dominated sorting genetic algorithm-II (NSGA-II), with results of 4.7% reduction in total annual cost (TAC) and 4.3% reduction in global energy consumption (GEC).

Insulation Structure Design for ±550 kV DC GIS Based on Multi-objective Optimization Algorithm

Insulation Structure Design for ±550 kV DC GIS Based on Multi-objective Optimization Algorithm

A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization

Multimodal multiobjective optimization problems (MMOPs) are a challenging class of problems. Several advanced evolutionary algorithms have been developed to solve MMOPs, but these algorithms still have some limitations, such as having many parameters, slow convergence speed, and unsatisfactory performance. To solve these problems, we propose a repulsive-distance-based maximum diversity selection algorithm (RMDS) which aims, during environmental selection, to select individuals with the best comprehensive diversity through the repulsive distance. The repulsive distance is the comprehensive Euclidean distance between an individual and selected individuals with the consideration of distribution of individuals in both the decision and objective spaces. RMDS has the following advantages: first, the repulsive distance allows rational selection of well-distributed solutions in the non-parameterized case. Second, the repulsive distance acts both in the decision and objective spaces, so it provides a good balance between the distribution of the solution set in these two spaces. Third, RDMS has a straightforward principle. Experimental results show that RMDS has superior performance incomparison with other well-known multimodal multiobjective evolutionary algorithms on 31 test functions.

An Approach to Near-Optimal Continuous-Thrust Solution for Plane Constellation Deployment

Paving the way in satellite constellation deployment, this article presents the Comprehensive Program (GCP) for planning constellation deployment missions by introducing a near-optimal solution for continuous-thrust maneuvers. The GCP establishes time constraints, enabling appropriate time scheduling and eliminating reconfiguration phases. It considers various cases of satellite departure and arrival sequences, collision avoidance constraints, and time limitations. A near-optimal solution for the continuous-thrust trajectory of each satellite is introduced, relying on the Fourier series approximation of the thrust pointing angle. The Fourier series with constant coefficients replaces the thrust angle in the satellite’s orbital motion equations in polar coordinates. The primary advantage of this near-optimal approach lies in its simplicity in accommodating space perturbations, posing no challenges in problem-solving. Additionally, the approach is beneficial due to its straightforward implementation and simple mathematical computations. This approach does not require pre-determining the revolution number of the transfer trajectory, unlike shape-based methods. The proposed method’s flexibility allows it to adapt and optimize the trajectory without prior assumptions about the number of revolutions required for the mission. Analyses demonstrate the proposed algorithm’s versatility and precision across various scenarios involving different orbital eccentricities, thrust forces, and thrust pointing angles. The constant coefficients of the Fourier series are determined by defining a set of objective functions and minimizing them using the Multi-Objective Particle Swarm Optimization algorithm (MOPSO). The first and second objective functions ensure that the transfer orbit reaches the desired deployment point, while the third and fourth ones guarantee the tangential conditions between the transfer orbit and the final orbit. An additional objective function minimizes the trajectory time. As a perturbation of interest, the effect of Earth’s oblateness is considered.

Optimal Allocation of Distributed Generation and Distribution Static Compensator Using Improved Multi-Objective Function and INFO Optimization Algorithm

This paper presents an improved multi-objective function (MOF) by simultaneously applying the voltage stability index (VSI) and system loadability (SL) for sustainable operation of a distribution network via the optimum location and sizing of distributed generations (DGs) and distribution static compensators (DSTATCOMs). In other words, with the approach of increasing the voltage profile, first, the candidate busbar for installation is determined based on the VSI, and then, by applying higher weights in the mentioned function, more priority is given to these busbars. This function is optimized by weighting candidate busbars using the INFO algorithm. The system loadability is included in the multi-objective function along with the total costs (involving the investment costs of distribution static compensators and distributed generations) and network loss. The sizes of the DGs and DSTATCOMs are enhanced using an INFO optimization algorithm. The operational economic benefits are represented by calculating the annual cost savings. The effectiveness of the proposed methodology is evaluated on 33- and 69-node distribution networks. Based on the findings, the proposed methodology is found to be more effective compared to other methods reported in the literature, and it provides more optimal answers compared to the non-consideration of the VSI.

Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective.

Global climate change has significantly increased the frequency and intensity of extreme precipitation events, thereby heightening flood risks for mountainous settlements. However, due to geographical and socio-economic constraints in these regions, flood-related sample data are generally scarce. This study introduces a Mean Filter (MF) grounded in the point-area duality perspective, combined with a feature selection approach utilizing multi-objective optimization algorithms. Three such algorithms (NSGA-II, SPEA2, and NSPSO) were applied to generate the Pareto front of flood susceptibility features, followed by a comprehensive evaluation using the CRITIC-TOPSIS method. The results show that NSGA-II performed best in terms of Hypervolume, Spacing, Generational Distance, and the number of Pareto solutions. The selected feature subsets demonstrated improvements across multiple model validations, with the GB model achieving the highest overall performance and stability. Notably, half of the retained features are related to buffer zone and watershed characteristics, and the SHAP analysis indicates that the mean Topographic Wetness Index within the 50-m buffer zone is the main contributor to flood risk. This finding validates the effectiveness of the MF strategy based on the point-area duality perspective under limited sample conditions, offering a new approach for flood studies with sparse data in mountainous regions.

EMO-TS: An Enhanced Multi-Objective Optimization Algorithm for Energy-Efficient Task Scheduling in Cloud Data Centers

EMO-TS: An Enhanced Multi-Objective Optimization Algorithm for Energy-Efficient Task Scheduling in Cloud Data Centers

The Innovation and Transfer of Computer Science and Technology in Recent and Modern Railway Transportation

This article aims to discuss the influence of computer technology on the innovation and technology transfer of modern railway transportation. By historical analysis and empirical research, this article first reviews the evolution of railway transportation from steam locomotive to High-Speed Railways, and then compares and analyzes the performance of the improved multi-objective particle swarm optimization (MPSO) algorithm and traditional particle swarm optimization (PSO) algorithm in train operation scheduling through designing simulation experiments. The experiment selected the representative train operation data sets, and the first 25 and 15 data items are used as training sets respectively to predict the subsequent data items, and the prediction results of MPSO algorithm, traditional PSO algorithm and support vector machine algorithm are compared. The results show that MPSO algorithm is excellent in prediction accuracy, convergence speed and error control, and its prediction results are closer to the actual train running state, which verifies the effectiveness of computer technology in optimizing railway transportation scheduling.