Multi-objective Evolutionary Algorithm Research Articles

Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.

Multi-objective evolutionary algorithm-enabled multi-stage collaborative scheduling for automotive production

In the automotive industry, considering all the process workshops as a whole in terms of production scheduling becomes much more significant for the enhancement and optimization of overall productivity, efficiency, resource utilization, and coordination among factories. However, the complicated operational interdependencies between workshops make it hard to acquire a global objective. This paper aims to model the collaborative scheduling problem for a multi-stage automotive production process first, involving critical decision variables from four main workshops, stamping, welding, painting, and assembling. Then, the multi-objective evolutionary algorithm based on Pareto optimal subspace learning (MOEA/PSL) associated with an encoding and decoding strategy based on a random key is designed to solve the model for minimizing the total cost and weighted tardiness. Finally, a real-life case study is carried out to illustrate the effectiveness and superiority of the proposed method via experimental comparison using practical data and simulated instances for further analysis.

Boosting scalability for large-scale multiobjective optimization via transfer weights

Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple conflicting objectives with hundreds or even thousands of decision variables, demand increasing computational resources to assure satisfactory performance as the decision variables increase. Multiobjective evolutionary algorithms are naturally scalable, but as the dimension increases, the conflict between analyzing enough solutions and the limited number of function evaluations has hindered further improvements in scalability. In this paper, we first define the scalability of multiobjective evolutionary algorithms and design an indicator to quantitatively measure the scalability. Second, to boost scalability when solving LSMOPs, we propose a scalable multiobjective optimization algorithm by transferring weights between solutions to reduce dependency on ever-increasing computational resources as the problem dimension increases. The proposed framework entails constructing a latent decision space to determine evolutionary weights for chosen representative solutions. These computed weights are then transferred to the remaining solutions, enabling evolutionary optimization to proceed without requiring additional evaluations, even as the dimensionality increases. By utilizing the knowledge gained from the source solutions, each solution is customized with an evolutionary weight scheme that not only preserves computational resources but also enhances optimization performance, thereby boosting scalability. We have conducted experiments on LSMOPs to verify the effectiveness and scalability of the proposed algorithm. The proposed method outperforms selected state-of-the-art algorithms and gains scalability boosts in situations where the dimensions increase while the function evaluation remains constant.

Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining—Application to Arbitrary Lagrangian Eulerian Orthogonal Cutting of Ti6Al4V

Abstract The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian–Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.

A hybrid multi-objective evolutionary algorithm with high solving efficiency for UAV defense programming

Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations face two main challenges: 1) Constructing a multi-objective optimization model reflecting the complexities of new UAV application patterns, and 2) solving the time-consuming WTA problem with high-dimension decision variables as it is NP-complete. This paper introduces a modified differential evolution based on a new mapping method called Pareto-Optimal-Matching that solves multi-objective binary optimization problems and constructs a 3 objective Sensor-Weapon-Target Assignment model. The proposed algorithm overcomes the inherent limitation of traditional differential evolution, which only performs well in continuous problems. Promising results were obtained based on different simulation metrics under various problems (proposed model, MOKP, and ZDT5), validating the superior performance of the proposed algorithm.

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

Evolutionary algorithms have been widely applied for solving multi-objective optimization problems, while the feature selection in classification can also be treated as a discrete bi-objective optimization problem if attempting to minimize both the classification error and the ratio of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) may have drawbacks for tackling large-scale feature selection, due to the curse of dimensionality in the decision space. Therefore, in this paper, we concentrated on designing an multi-task decomposition-based evolutionary algorithm (abbreviated as MTDEA), especially for handling high-dimensional bi-objective feature selection in classification. To be more specific, multiple subpopulations related to different evolutionary tasks are separately initialized and then adaptively merged into a single integrated population during the evolution. Moreover, the ideal points for these multi-task subpopulations are dynamically adjusted every generation, in order to achieve different search preferences and evolutionary directions. In the experiments, the proposed MTDEA was compared with seven state-of-the-art MOEAs on 20 high-dimensional classification datasets in terms of three performance indicators, along with using comprehensive Wilcoxon and Friedman tests. It was found that the MTDEA performed the best on most datasets, with a significantly better search ability and promising efficiency.

Cooperative constrained multi-objective dual-population evolutionary algorithm for optimal dispatching of wind-power integrated power system

Due to increasing uncertainty and multiple operational requirements of current power system, it brings great challenge to power dispatch of wind-power integrated power system. To address above problems, a cooperative constrained multi-objective dual-population evolutionary algorithm (CCMDEA) is proposed with uncertainty budget of wind power, which divides the intermittent power into different intervals with flexible uncertain parameters. On the basis of non-dominated sorting genetic algorithm III (NSGA-III), constraint violation (CV) is also involved to guide the population evolution combined with two defined populations: convergence population (CP) and diversity population (DP) to coordinate convergence and diversity performance of pareto-fronts, and a coordinated selection mechanism is designed to select suitable parents according to the state of evolutionary population, which can coordinate the convergence population and diversity population well. According to simulation results on two test systems, the efficiency and priority of the proposed algorithm is well verified, which also reveals that the proposed algorithm can be a viable way for solving power dispatching of wind-power integrated power system.

Dual population multi-objective evolutionary algorithm for dynamic co-transformations

Dual population multi-objective evolutionary algorithm for dynamic co-transformations

Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection

The hardly dominated boundary (HDB) is a common feature of multi-objective optimization problems (MOPs). Previous studies have proposed several multi-objective evolutionary algorithms (MOEAs) to deal with the problem characterized by HDBs. Nevertheless, these methods lack consideration of the evolutionary status, potentially resulting in low efficiency when addressing problems that entail a confluence of HDB and other intricate characteristics. This paper proposes an MOEA with evolutionary-status-driven environmental selection (MOEA-ESD) to address such an issue. Specifically, in each generation, the current and historical information are used to evaluate the evolutionary status. Based on the estimated evolutionary status, the proposed MOEA-ESD algorithm adaptively uses three types of environmental selection: the traditional environmental selection (TES) based on Pareto-dominance and crowding distance, a convergence-first environmental selection (CFES) based on Gaussian mixture model clustering, and a diversity-first environmental selection (DFES) based on outlier detection and extreme solutions. In this way, inferior solutions situated on the HDB can be effectively eliminated, thereby facilitating the convergence of the population while concurrently preserving a commendable degree of diversity. Moreover, a set of new benchmark problems with different objective magnitudes and complicated Pareto sets is developed to enrich the features of HDB-MOPs and to verify the algorithm's performance. Our experimental results on 22 HDB-MOPs show the promising performance of the proposed algorithm. The source code of MOEA-ESD is available at https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/MOEA-ESD.

Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence

Background Cement-zeolite improved sand can be used in diverse civil engineering applications. However, earlier research has not duly optimized its production process to attain best mechanical strength, lowest cost, and least environmental impact. This study proposes a multi-objective optimization approach using back-propagation neural network (BPNN) to predict the mechanical strength, along with an adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) to identify the best parameters for cement-zeolite-improved sand, filling a long-lasting research gap. Methods A collection of unconfined compression tests was used to evaluate cemented sand specimens treated with stabilizers including portland cement (at dosages of 2, 4, 6, 8, and 10%) and six dosages of natural zeolite as partial replacement for cement (0, 10, 30, 50, 70, and 90%) at different curing times of 7, 28, and 90 days. The study further conducts a detailed analysis of life cycle assessment (LCA) to show how partial zeolite replacement for cement impacts the environment. Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. This allowed the AGE-MOEA to optimize zeolite and cement dosages, density, curing time, and environmental impact. Results The results of this study reveal that the optimal range of zeolite was between 30-45%, which not only increased cemented sand strength, but also reduced the cost and environmental impact. It is also shown that increasing the zeolite replacement to 25-30% can increase the ultimate strength of cemented sand, yet exceeding this limit can cause the strength to decrease. Conclusions Zeolite has the potential to serve as an alternative for cement in applications that involve cemented sand, while still achieving mechanical strength performance, which is comparable or even superior. From an LCA standpoint, using zeolite as partial cement replacement in soil improvement projects is a promising alternative.

Risk control of epidemic in urban cold-chain transportation

The COVID-19 pandemic has severely disrupted the daily running of urban logistics system, thereby increasing the resilience and health requirements in the design of sustainable logistics system. Most scientific contributions have enriched the knowledge of how to deal with viral infection threats from the external environment but ignored the virus transmission from fomites within the logistics system. This study designs an urban cold-chain logistic network with control strategies to reduce the epidemic risk caused by cold-chain fomites while concurrently balancing the total cost and customer satisfaction, which is modeled as a multi-objective vehicle routing problem (MOVRP). The control strategies are modeled by dividing customer points into small groups, and the virus transmission in populations is considered when evaluating epidemic risk. A multi-objective evolutionary algorithm with special initialization (MOEA-SI) is proposed to solve this problem. Simulation results show that all the strategies can efficiently reduce the epidemic risk with reasonable expense of total cost and customer satisfaction. The introduction of special initialization strategy enables the MOEA-SI to have good performance in balancing the three objectives. The above results can enrich the knowledge of how to deal with unexpected epidemics and help the scientific decision-making in sustainable urban management.

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

A recursive framework for improving the performance of multi-objective differential evolution algorithms for gene selection

Gene selection is a pivotal process in machine-learning-driven medical diagnostics, where the goal is to identify a subset of genes from microarray expression profiles that can enhance the predictive accuracy of classifiers for disease diagnosis. The two key objectives of gene selection are to reduce the dimensionality of the data and to improve the accuracy of disease diagnosis, which is typically a multi-objective optimization problem. In recent years, multi-objective evolutionary algorithms (MOEAs) have gained wide attention in feature selection research, and several related algorithms have been produced. However, most algorithms tend to get stuck in local optimality when searching for solutions from a high-dimensional space. To solve the gene selection problem effectively, this study introduces a recursive multi-objective differential evolution algorithm with elite recursive strategy (RMODE-E) and a recursive multi-objective differential evolution algorithm with Pareto front recursive strategy (RMODE-P). RMODE-E amalgamates the features selected by the top E elite individuals, RMODE-P consolidates the features selected by the Pareto front set, and the combined features then serve as the foundation for subsequent recursive rounds of searching. The proposed feature subspace combination strategy not only reduces the recursive search space but also improves the capacity to find globally optimal feature subsets. Extensive experiments were conducted to compare our proposed algorithms with eight state-of-the-art evolutionary algorithms to validate their effectiveness. Experimental results demonstrate that RMODE-P has better global search capability as it achieves better best classification accuracy, mean classification accuracy, and minimal gene subset size.

Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA

This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker’s preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3).

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Multi-objective electricity generation expansion planning towards renewable energy policy objectives under uncertainties

Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches. The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts, and incorporate changing policy objectives into the constraints to meet policy makers’ expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system. The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers. Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made.

Subset Selection for Evolutionary Multiobjective Optimization

Subset selection, which selects a subset of solutions according to certain criterion/indicator, is a topic closely related to evolutionary multi-objective optimization (EMO). The critical component of a multi-objective evolutionary algorithm (MOEA), environmental selection, is essentially a subset selection problem, i.e., selecting N solutions as the next-generation population from usually 2N candidates (where N denotes the size of the population). Another use of subset selection is the solution pre-processing procedure for decision-making, in which typically a few representatives are selected from the final population or a large-size archive, in order not to overwhelm the decision maker. Existing work for subset selection in EMO is focused on developing greedy algorithms, but may suffer from being trapped in local optima. In this paper, we approach the problem by providing a multi-objective evolutionary algorithmic framework. We consider several popular quality indicators for subset evaluation and present accelerated variants of a well-studied MOEA in the theoretical study area, Global Simple Evolutionary Multi-objective Optimizer (GSEMO), for each indicator. We conduct rigorous theoretical analyses of the acceleration procedure. Moreover, we prove that our algorithms can achieve the best-so-far approximation guarantee by the submodularity of the indicators. We finally empirically show the effectiveness and scalability of the proposed algorithms, in addition to the potentials to be further improved by introducing popular MOEAs (e.g., using NSGA-II and MOEA/D to replace GSEMO).

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

Offline Data-Driven Multiobjective Optimization Evolutionary Algorithm Based on Generative Adversarial Network

Usually, data-driven multi-objective optimiza -tion problems (DD-MOPs) are indirectly solved by evolutionary algorithms through the built surrogate model which is well-trained from sample data. However, in most DD-MOPs, only a few available data can be practicably collected from real engineering experiments due to the unaffordable cost and time. The key challenge in such a DD-MOP is to prevent the serious deterioration on the accuracy of the obtained approximate Pareto front. In this paper, two novel strategies, critical fitness for evolutionary algorithms and data augmentation for a surrogate model, are complementarily imposed by a generative adversarial network (GAN) to tackle with the challenges in DD-MOPs. In the critical fitness strategy, a new critical fitness, composed of the critical score from the discriminator of GAN and the prediction value of surrogate model, is proposed to improve the accuracy of approximate Pareto front of a DD-MOP. In the data augmentation strategy, some new samples are synthetized by the generator of GAN to build a better-trained surrogate model. As a result, the GAN concurrently serves the critical fitness strategy and the data augmentation strategy as the roles of “killing two birds with one stone”. The performance of the proposed algorithm for DD-MOPs was well-verified over 26 benchmark problems and successfully applied to discover new NdFeB materials.

Using multiobjective optimization to reconstruct interferometric data

Context. In very long baseline interferometry (VLBI), signals recorded at multiple antennas are combined to form a sparsely sampled virtual aperture with an effective diameter set by the largest separation between the antennas. Due to the sparsity of the sampled aperture, VLBI imaging constitutes an ill-posed inverse problem. Various algorithms have been employed to deal with the VLBI imaging, including the recently proposed multiobjective evolutionary algorithm by decomposition (MOEA/D) described in the first paper of this series. Aims. Among the approaches to the reconstruction of the image features in total intensity from sparsely sampled visibilities, extensions to the polarimetric and the temporal domain are of great interest for the VLBI community in general and the Event Horizon Telescope Collabroration (EHTC) in particular. Based on the success of MOEA/D in presenting an alternative claim of the image structure in a unique, fast, and largely unsupervised way, we study the extension of MOEA/D to polarimetric and time dynamic reconstructions in this paper. Methods. To this end, we utilized the multiobjective, evolutionary framework introduced for MOEA/D, but added the various penalty terms specific to total intensity imaging time-variable and polarimetric variants, respectively. We computed the Pareto front (the sample of all non-dominated solutions) and identified clusters of close proximities. Results. We tested MOEA/D with synthetic data sets that are representative for the main science targets and instrumental configuration of the EHTC and its possible successors. We successfully recovered the polarimetric and time-dynamic signature of the ground truth movie (even with relative sparsity) and a set of realistic data corruptions. Conclusions. MOEA/D has been successfully extended to polarimetric and time-dynamic reconstructions and, specifically, in a setting that would be expected for the EHTC. It offers a unique alternative and independent claim to the already existing methods, along with a number of additional benefits, namely: it is the first method that effectively explores the problem globally and compared to regularized maximum likelihood (RML) methods. Thus, it waives the need for parameter surveys. Hence, MOEA/D is a novel, useful tool to characterize the polarimetric and dynamic signatures in a VLBI data set robustly with a minimal set of user-based choices. In a consecutive work, we will address the last remaining limitation for MOEA/D (the number of pixels and numerical performance), so that MOEA/D can firmly solidify its place within the VLBI data reduction pipeline.