Set Of Non-dominated Solutions Research Articles

HV-Net: Hypervolume Approximation Based on DeepSets

In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network with permutation invariant property, to approximate the hypervolume of a non-dominated solution set. The input of HV-Net is a non-dominated solution set in the objective space, and the output is an approximated hypervolume value of this solution set. The performance of HV-Net is evaluated through computational experiments by comparing it with two commonly-used hypervolume approximation methods (i.e., point-based method and line-based method). Our experimental results show that HV-Net outperforms the other two methods in terms of both the approximation error and the runtime, which shows the potential of using deep learning techniques for hypervolume approximation.

Mission and Reliability Driven Fleet-Level Selective Maintenance Planning and Scheduling Two-Stage Method

This paper studies the problem of planning and scheduling in selective maintenance tasks of mission requirements and the health condition of the fleet. In order to deal with the problems of high maintenance cost and long time consumption in maintenance systems, a two-stage fleet maintenance optimization method is proposed. Firstly, a selective maintenance model of fleets based on age reduction is established to maximize the probability of completing the next mission and minimize the maintenance cost. Secondly, a multiobjective sparrow search algorithm is designed to solve the maintenance planning problem in the first stage, and a nondominated solution set of maintenance strategies satisfying the mission constraint is obtained. In the second stage, the simulated annealing algorithm is used to schedule the maintenance task and obtain the minimum maintenance hours required by the maintenance strategy. An example analysis of a vehicle fleet is launched to prove the effectiveness of this method. In a word, this method not only meets the mission requirements but also achieves the purpose of reducing maintenance cost and maintenance hours, which can provide reference for other types of equipment maintenance.

A multi-objective strategy for cost-effective microgrid solutions based on renewable energy sources

With the rapid urbanization and increasing energy demands, microgrids are recognizing renewable energy sources (RESs) as a valuable power generation option. However, efficiently managing the energy cost poses a significant challenge in integrating RESs with microgrids. To address this challenge, this study presents a novel approach utilizing a cost-effective multi-objective genetic algorithm (MOGA) to optimize power allocation among diverse generation units within the microgrid. The proposed MOGA algorithm aims to minimize generation costs by efficiently distributing the generated power from different sources in the microgrid vs the CO2 emission. By leveraging the genetic algorithm population, MOGA generates a diverse set of non-dominated solutions. Simulation results demonstrate the effectiveness of the proposed approach in reducing the cost of RESs in microgrids, surpassing the performance of other multi-objective optimization methods such as multi-objective particle swarm (MOPSO) and multi-objective wind-driven optimization (MOWDO).

Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance—consisting of achieving the optimum period of time to carry out a preventive maintenance activity—are taken into account before being considered by the authors of the present paper. This work couples Multi-objective Evolutionary Algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the Multi-Objective Evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed Multi-objective Evolutionary Algorithms is handled; moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of Multi-objective Evolutionary Algorithms and Discrete Event Simulation. Finally, the methodology is expanded to more complex systems which are successfully solved.

INFORM: Inverse Design Methodology for Constrained Multiobjective Optimization

Many system design methods use population-based optimization or a surrogate model for solving constrained multi-objective optimization. When designing a system with multiple objectives and constraints, the designer may first be interested in understanding the trade-offs among different objectives from a small number of simulations. In the next step, the designer may focus on specific regions of interest in the design space near a set of non-dominated solutions to further improve performance on the targeted objectives. This may help make the search process sample-efficient. We propose INFORM: a two-step approach for sample-efficient constrained multi-objective optimization of real-world nonlinear systems. In the first step, we modify a genetic algorithm (GA) to make the design process sample-efficient. We inject candidate solutions into the GA population using inverse design methods instead of determining the candidate solutions for the next generation using only crossover and mutation, as is done in standard GA. We present three types of inverse design techniques based on a (i) neural network verifier, (ii) neural network, and (iii) Gaussian mixture model. The candidate solutions for the next generation are thus a mix of those generated using crossover/mutation and solutions generated using inverse design. At the end of the first step, we obtain a set of non-dominated solutions. In the second step, we choose the regions of interest around the non-dominated solutions to further improve the objective function values using inverse design methods. We demonstrate the efficacy of INFORM through synthesis of nonlinear systems and analog circuits. The experimental results show that INFORM reduces synthesis time by up to 29× and improves the value of the objective function by up to 33% compared to a state-of-the-art baseline design methodology.

Optimal Capacity and Location for STATCOM with Seasonal Wind Power Prediction Using C-Vine Copula

This paper proposes a selection strategy for determining the optimal capacity and location of a static synchronous compensator (STATCOM) device under intermittent output variations of renewable energy sources using a multi-objective cuckoo search (MOCS) algorithm. The impact of seasonal output variations on the location and capacity of the STATCOM devices was analyzed based on the daily output variations of the wind turbines. Three objective functions were considered to determine the optimal location and capacity of the STATCOM devices: (1) minimizing the system line losses, (2) minimizing the transient voltage indicators, and (3) minimizing the total cost of the STATCOM capacity. MOCS was combined with the Pareto front to determine the non-dominated solution sets for the four seasons. The Manhattan distance method was used to select the most suitable installation locations and capacities within a specified time interval. Power flow calculations were performed using DIgSILENT Power Factory 2021 and MATLAB R2021b. To validate the proposed method, a test experiment was conducted using an IEEE 39 bus system.

A multi-objective mixed integer linear programming model for thesis defence scheduling

In this paper, we address the thesis defence scheduling problem, a critical academic scheduling management process, which has been overshadowed in the literature by its counterparts, course timetabling and exam scheduling. Specifically, we address the single defence assignment type of thesis defence scheduling problems, where each committee is assigned to a single defence, scheduled for a specific day, hour and room. We formulate a multi-objective mixed-integer linear programming model, which aims to be applicable to a broader set of cases than other single defence assignment models present in the literature, which have a focus on the characteristics of their universities. For such a purpose, we introduce a different decision variable, propose constraint formulations that are not regulation and policy specific, and cover and offer new takes on the more common objectives seen in the literature. We also include new objective functions based on our experience with the problem at our university and by applying knowledge from other academic scheduling problems. We also propose a two-stage solution approach. The first stage is employed to find the number of schedulable defences, enabling the optimisation of instances with unschedulable defences. The second stage is an implementation of the augmented ϵ-constraint method, which allows for the search of a set of different and non-dominated solutions while skipping redundant iterations. The methodology is tested for case-studies from our university, significantly outperforming the solutions found by human schedulers. A novel instance generator for thesis scheduling problems is presented. Its main benefit is the generation of the availability of committee members and rooms in availability and unavailability blocks, resembling their real-world counterparts. A set of 96 randomly generated instances of varying sizes is solved and analysed regarding their relative computational performance, the number of schedulable defences and the distribution of the considered types of iterations. The proposed method can find the optimal number of schedulable defences and present non-dominated solutions within the set time limits for every tested instance.

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

Generating balanced workload allocations in hospitals

As pressure on healthcare systems continues to increase, it is becoming more and more important for hospitals to properly manage the high workload levels of their staff. Ensuring a balanced workload allocation between various groups of employees in a hospital has been shown to contribute considerably towards creating sustainable working conditions. However, allocating work to different organizational units in a fair manner is not straightforward when it involves complex decision-making processes. In this paper we set out to balance the workload of heterogeneous hospital wards by optimizing the patient admission scheduling problem. Given the multi-period nature of patient admission scheduling, we introduce a new equity objective that captures both spatial (between hospital wards) and temporal (between days in the planning period) workload balancing. The resulting bi-objective problem is solved using an exact criterion space search algorithm. Our computational study employs problem instances that have been generated based on real-world data. The results demonstrate how spatially and temporally balanced workload allocations can be generated by minimizing the proposed equity objective. Moreover, we analyze sets of nondominated solutions to gain various insights into the trade-off between schedule cost and workload balance.

Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification.

Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting nondominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multiobjective optimization in improving the generalization.

Integrated multiple criteria decision-making framework for ranking Pareto optimal solutions of the multiobjective optimization problem of tuned mass dampers

Tuned mass dampers (TMD) have demonstrated effectiveness and robustness in controlling vibrations in marine structures. Solutions to the TMD's Multi-objectives optimization problem (MOOP) always yield a set of Pareto compromise solutions for different conflicting objectives. However, because all the Pareto solutions are non-dominated, the solution of the MOOP still needs to be completed since a single Pareto solution must be selected from a large set of Pareto solutions. Researchers in the literature concentrated on obtaining the Pareto front by solving the MOOP of the TMD for different objective functions. Nonetheless, no effort has been put into selecting the best solution from the Pareto set. Therefore, this paper presents the novel application of the multiple-criteria decision-making (MCDM) techniques in the solution of the MOOP of the TMD applied to a marine structure. The presented framework ensures that the decision-makers (DMs) obtain the most optimal solution from the Pareto set of solutions of the TMD. In this paper, the problem of the TMD applied to a jacket platform is formulated as a MOOP, which comprises conflicting objectives related to TMD cost and performance. NSGA-II is utilized to find a large set of non-dominated TMD tuning parameters from which the DMs must select only one solution. Then, the DMs' preferences are prioritized using the Analytical Hierarchy Process (AHP). Meanwhile, the reasonableness of the DMs' preferences is assessed. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized for ranking the non-dominated set of solutions by assessing the relative closeness of each solution to the ideal solution and farness away from the negative ideal solution. A Finite Element (FE) model of a fixed offshore platform under the dynamic disturbance of irregular waves is numerically solved using COMSOL multiphysics to assess the goals mentioned earlier. A TMD is applied to the marine structure to mitigate wave-induced vibrations. The hydrodynamic forces affecting the structure are computed using Morison's equation, and the irregular waves are characterized using the Joint North Sea Wave Project (JONSWAP) wave spectrum. The presented framework will be used for ranking the Pareto solutions of the TMD applied to the deck of the FE model. The TMD tuning parameters considered in this regard are the TMD mass ratio μ, damping ratio ξopt, and frequency ratio fopt. The findings indicate that the framework presented provides an efficient decision-making methodology for selecting the best solution in various design scenarios. These scenarios can be presented in 2D, 3D, or high-dimensional objective space. Meanwhile, the DMs will be able to select the best compromise among the objectives without the need for numerical judgments. As a result, an efficient TMD that provides valuable response mitigation in a low-cost and effective space was presented.

Differential Evolution-Based Feature Selection: A Niching-Based Multiobjective Approach

Feature selection is to reduce both the dimensionality of data and the classification error rate (i.e., increase the classification accuracy) of a learning algorithm. The two objectives are often conflicting, thus a multiobjective feature selection method can obtain a set of nondominated feature subsets. Each solution in the set has a different size and a corresponding classification error rate. However, most existing feature selection algorithms have ignored that, for a given size, there can be different feature subsets with very similar or the same accuracy. This article introduces a niching-based multiobjective feature selection method that simultaneously minimizes the number of selected features and the classification error rate. The proposed method conceives to identify: 1) a set of feature subsets with good convergence and distribution and 2) multiple feature subsets choosing the same number of features with almost the same lowest classification error rate. The contributions of this article are threefold. First, a niching and global interaction mutation operator is proposed that can produce promising feature subsets. Second, a newly developed environmental selection mechanism allows equal informative feature subsets to be stored by relaxing the Pareto-dominance relationship. Finally, the proposed subset repairing mechanism can generate better feature subsets and further remove the redundant features. The proposed method is compared against seven multiobjective feature selection algorithms on 19 datasets, including both binary and multiclass classification tasks. The results show that the proposed method can evolve a rich and diverse set of nondominated solutions for different feature selection tasks, and their availability helps in understanding the relationships between features.

Intelligent fire information system based on 3D GIS

BackgroundThis work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. MethodsThe intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. ResultsThe experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t=17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. ConclusionsThe IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

A knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation

To enhance the segmentation performance and optimization efficiency of multi-objective evolutionary clustering algorithms for noisy images, this study presents a knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation (K2 MORFC). First, pixel and region-level fitness functions are developed to comprehensively consider the spatial constraints and region consistency in an image. In the pixel-level fitness function, a Kullback–Leibler (KL) divergence-based spatial constraint term is employed and combined with intra-class compactness to preserve the membership similarity between an arbitrary image pixel and its non-local neighbors. In the region-level fitness function, image edge information is utilized to evaluate the regional consistency of the partition. Moreover, an adaptive determination mechanism of the weight factor for the spatial constraint term is designed in the pixel-level fitness function to control the influence of the spatial constraint for each pixel. Furthermore, considering that the preference toward knee points can effectively improve the convergence performance, a knee point driven Kriging-assisted evolutionary algorithm (K2EA) is proposed to efficiently optimize these two fitness functions, including a dynamic subspace knee point searching strategy, a knee point guided environmental selection strategy, and a Kriging model management mechanism. Finally, we construct a fuzzy clustering validity index with a KL divergence-based non-local spatial constraint term to select the optimal solution from the nondominated solution set. Competitive experimental results on the DTLZ benchmark verify the effectiveness of K2EA. Segmentation results on color images from the Berkeley dataset and magnetic resonance images from the BrainWeb and IBSR datasets confirm the performance of K2MORFC.

A bi-objective model for territorial design

The clustering of spatial-geographic units, zones or areas has been used to solve problems related to Territorial Design. Clustering adapts to the definition of territorial design for a specific problem, which demands spatial data processing under clustering schemes with topological requirements for the zones. For small sized instances, once the geographical compactness is attended to as an objective function, this problem has been solved by exact methods with an acceptable response time. However, for larger instances and due to the combinatory nature of this problem, the computational complexity increases and the use of approximated methods becomes a necessity, in such a way that when geographical compactness was the only cost function a couple of approximated methods were incorporated giving satisfactory results. A particular case of this kind of problems that has had our attention in recent years is the classification by partitioning of AGEBs (Basic Geographical Units by its initials in Spanish). Some work has been made related to the formation of compact groups of AGEBs, but additional re-strictions were often not considered. A very interesting and highly demanded application problem is to extend the compact clustering to form groups with for some of its descriptive variables. This problem translates to a multi-objective approach that has to pursue two objectives to attain a balance between them. At this point, to reach a set of non-dominated and non-comparable solutions, a method has been included that allows obtaining the Pareto front through the Hasse diagram, which implies proposing a mathematical programming model and the synthetic resulting between compactness and homogeneity.

Cluster-Based Multiobjective Particle Swarm Optimization and Application for Chemical Plants

In multiobjective particle swarm optimization (MOPSO), the global-best particle is randomly selected for each population particle from a nondominated solution set. However, this Roulette wheel-based global particle selection is ineffective for convergence and diversity when the problem has numerous decision variables or a large number of global-best candidates. Thus, this study proposes the cluster-based MOPSO (CMOPSO). In CMOPSO, the similarities between particles are considered when selecting the global-best particle. The cluster for each particle is determined based on the Euclidean distance in the decision or objective space. The proposed approach is demonstrated by applying an operating condition optimization problem to the hydrogen production process. The target process is a representative chemical plant with a large search space and strong nonlinearity. Furthermore, the performance of CMOPSO is assessed by comparing it with that of MOPSO. The results indicate that CMOPSO considered in the decision space exhibits superior performance in terms of convergence and diversity.

Prediction of complex public opinion evolution based on improved multi-objective grey wolf optimizer

In an emergency, predicting the evolution trends of network public opinion is of great significance for governments to manage the situation and associated social public opinion trends. Most of the existing public opinion prediction models have the disadvantages of long time consumption, limited types of predicted public opinion categories and low-accuracy prediction results. To improve the ability to predict the evolutionary trend of complex public opinion, an improved multiobjective gray wolf optimizer (IMOGWO) is proposed. IMOGWO uses logistic and Lotka-Volterra models to initialize the wolf population and improve the population validity; it designs a nonlinear function to adjust the population update factor to improve the exploration and exploitation ability and the local search ability of the wolf population; and it introduces the elite retention policy and a Pareto-optimal solution set to achieve multiple objectives using the principle of a nondominated solution set. In simulation experiments, various network emergencies were taken as empirical analysis cases. The experimental results show that the improved multiobjective GWO has good accuracy and universality on complex public opinion evolution prediction and can better predict the evolution trends of various types of complex public opinion than other tested methods.

A multi-objective approach for reducing Patient’s inconvenience in a generalized home healthcare delivery setup

In this work, we present a mixed-integer programming model for a multi-objective home healthcare delivery problem. The proposed problem is modeled with minimum assumptions about the procedure attributes and can handle most of the commonly imposed restrictions in the field of home healthcare delivery. Under the imposed restrictions, the model is designed to provide selection (for caregivers and patients), assignment, scheduling, and routing decisions. In addition to some minor modifications in ‘workload balance’ constraints, a major focus of the work is to improve the quality of the schedule for the selected patients. To achieve this, we define and calculate the inconvenience caused by the unnecessarily scattered visits and their overlap with the patient-specific inconvenient time window. The model minimizes the total inconvenience cost for patients against the competitive goals of other stakeholders. Higher net profit, minimum loss of employed labor, balanced workload among staff, and maximization of fully served patients have been included as the other objectives. To solve the instances of the proposed home healthcare delivery problem, an efficient implementation for a reference point-based non-dominated sorting genetic algorithm (NSGA-III) is developed. After extensive parameter tuning using the Taguchi method of experimental design, the algorithm is used for generating a diverse set of non-dominated solutions for the decision-maker. In addition to the comparing the performance of NGSA-III with Multi-objective particle swarm optimization and Multi-objective grey wolf optimizer, experiments are also carried out to establish the relationship between patient convenience and net profit.

Batched Data-Driven Evolutionary Multiobjective Optimization Based on Manifold Interpolation

Multiobjective optimization problems are ubiquitous in real-world science, engineering, and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of nondominated tradeoff solutions, where the expensive objective functions are approximated as a surrogate model. In this article, we propose a framework for implementing batched data-driven evolutionary multiobjective optimization (EMO). It is so general that any off-the-shelf EMO algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush–Kuhn–Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against seven state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various Pareto-optimal front shapes.

Optimal Design of Agricultural Mobile Robot Suspension System Based on NSGA-III and TOPSIS

The stability of vehicles is influenced by the suspension system. At present, there are many studies on the suspension of traditional passenger vehicles, but few are related to agricultural mobile robots. There are structural differences between the suspension system of agricultural mobile robots and passenger vehicles, which requires structural simplification and modelling concerning suspension of agricultural mobile robots. This study investigates the optimal design for an agricultural mobile robot’s suspension system designed based on a double wishbone suspension structure. The dynamics of the quarter suspension system were modelled based on Lagrange’s equation. In our work, the non-dominated sorting genetic algorithm III (NSGA-III) was selected for conducting multi-objective optimization of the suspension design, combined with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) to choose the optimal combination of parameters in the non-dominated solution set obtained by NSGA-III. We compared the performance of NSGA-III with that of other multi-objective evolutionary algorithms (MOEAs). Compared with the second-scoring solution, the score of the optimal solution obtained by NSGA-III increased by 4.92%, indicating that NSGA-III has a significant advantage in terms of the solution quality and robustness for the optimal design of the suspension system. This was verified by simulation in Adams that our method, which utilizes multibody dynamics, NSGA-III and TOPSIS, is feasible to determine the optimal design of a suspension system for an agricultural mobile robot.