Computational Intelligence Methods Research Articles

Versatile unsupervised design of antennas using flexible parameterization and computational intelligence methods

Developing contemporary antennas is a challenging endeavor that requires considerable engineering insight. The most laborious stage is to devise an antenna architecture that delivers the required functionalities, e.g., multiband operation. Iterative by nature (hands-on topology modifications, parametric studies, trial-and-error geometry selection), it typically takes many weeks and requires considerable engagement from a human expert. Consequently, only a few possible design options concerning the fundamental antenna geometry may be considered. Automated topology rendition and geometry parameter optimization are highly relevant, especially from the industrial perspective. Therein, reducing time-to-market and limiting the involvement of trained experts is critical. This research proposes an innovative procedure for unsupervised development of planar antennas. Our method leverages flexible antenna parameterization based on re-sizable elliptical patches. It permits the realization of a massive number of geometries of diverse shapes and complexities using a small number of decision variables. Computational intelligence methods are employed to conduct antenna evolution exclusively based on specifications and possible constraints (e.g., maximum size). Fine-tuning of the structure geometry is achieved through low-cost local search routines. Our methodology is demonstrated by designing several antennas featuring distinct characteristics (broadband, single-, dual- and triple-band). The obtained results, supported by experimental data, underscore the presented approach’s versatility and capability to render unconventional topologies at reasonably low computational expenses. As mentioned earlier, the design process is fully automated without human expert involvement.

Computational and Human Intelligence Methods for Constructing Practical Risk Prediction Models: An Application to Cardio-Renal Outcomes in Non-Diabetic CKD Patients

The current investigation aimed to develop a novel approach for risk prediction modeling of clinical outcomes in common diseases based on computational and human intelligence techniques with no a priori input on risk factors using real-world individual patient-level data from administrative claims. Bootstrapping multivariable Cox regression and ant colony optimization were employed to develop time-to-first-event risk prediction models of cardio-renal outcomes in patients with non-diabetic chronic kidney disease (CKD) as a demonstration case. A cohort of 504,924 non-diabetic CKD stage 3 or 4 patients enrolled from 2008 to 2018 were identified in the US administrative de-identified claims database, Optum Clinformatics® Data Mart. Initial set of potential risk factors was derived from patient-level data at baseline and included more than 540,000 variables. Risk prediction models of hospitalization for heart failure, worsening of CKD stage from baseline and a renal composite outcome of end-stage kidney disease, kidney failure or need for dialysis in non-diabetic CKD stage 3 or 4 were built. Final model optimization was conducted using human intelligence to combine clinically similar features and build equivalence classes to ensure that risk factors included in the final model were routinely collected and easily interpretable by healthcare providers. Demonstrated validity of our approach in non-diabetic CKD offers opportunities for application in other therapeutic areas, with the potential to improve overall prognosis and decrease the clinical and economic burden of common diseases. The approach enables developing practical prediction models for risk estimation in routine clinical practice.

Exploring the link between foresight and artificial intelligence methods to strengthen collective future-building in contexts of social instability

Purpose The study explores the connection between foresight and Artificial Intelligence (AI) methods in a community within an environment of social instability in Colombia. It aims to contribute to research on aligning these methods for future-shaping, with the goal of enhancing shared governance, peer learning and collective learning among traditional decision-makers and local communities in emerging countries. The study seeks to foster a community of social actors who are likely to engage constructively in strategic dialogues. To enhancing shared governance and learning a hybrid model is synthesized, combining foresight and computational intelligence. Design/methodology/approach The case study explores the integration of computational intelligence and foresight through Gaston Bachelard's (Bachelard, 1936) phenomenology concept of ante-perception. The mathematical representation of the cone of scenarios provides a structured way to explore multiple future pathways, allowing communities to visualize and compare different trajectories and make informed decisions amid uncertainty. The model facilitates critical reflections on present assumptions, deepening insights into future scenarios. Ante-perception challenges traditional approaches to foresight by encouraging a break from established experiences, allowing for novel insights into possible futures. When enriched by computational intelligence, this reflective process is further strengthened by quantitative approach scenario modeling. Findings This research develops and tests a proposal that includes the logic and methods for constructing a mathematical representation of the cone of scenarios. This process, which is interactive and deliberative, is driven by anticipation and combines qualitative and quantitative approaches within a context of high uncertainty. By combining the critical reflection facilitated by ante-perception with the predictive power of computational intelligence, the model allows communities to transcend established thought patterns and explore innovative future possibilities. This integrative approach helps them envision and work toward social self-transformation. Research limitations/implications The article aims to identify the creation of scenarios in contexts of high uncertainty, to respond to the needs of communities in emerging countries to manage change. Practical implications This article explores a novel approach to using foresight for address collective intelligence by developing a shared future vision in high-uncertainty contexts within local communities in emerging countries. The application of the hybrid model demonstrates that foresight is a key innovative social tool for developing long-term strategic reflection and planning for territories. Social implications In developing long-term reflective processes, explaining phenomena, mechanisms and correlations requires the use of value judgments. This set of value judgments requires a representation that facilitates their treatment, helps to account for their behavior during the inference process to form a shared future vision. Consequently, ensuring the recognition of the opinions of local communities through participatory discussion spaces and their subsequent refinement, from a technical perspective, aims to illustrate the development of this social construction process. While similarities exist, differences add value through a transfer process, often subconscious. This process stimulates collective learning and builds capacities as knowledge is developed through inquiry, evaluation, interpretation and generalization. Originality/value This research provides a unique hybrid model that fosters collective learning and engagement by integrating local community perspectives with advanced computational intelligence methods. By facilitating both reflective and quantitative approach future-shaping, it offers a practical framework for addressing uncertainty while empowering communities to shape their own futures. It underscores the importance of recognizing local community views through bottom-up participatory discussions, thereby widening the stakeholder community to active engagement in addressing broader societal issues. The case study focuses on community collaboration in Puerto Gaitán, a Colombian municipality.

Refrigeration capacity modeling of europium titanate based magnetocaloric compounds using computational single hidden layer intelligent and random forest regression methods

Europium titanate (EuTiO3) is a quantum para-electric magnetocaloric compound with potential application in green magnetic cooling technology. Super-exchange and indirect interactions existing between the magnetic moments of this compound are responsible for the observed magnetic properties such as the refrigeration capacity which falls among the key factors that characterize the usefulness of magnetic system of cooling in addressing energy crisis. Crystallographic substitutions and ionic replacement potentially shift magnetic ordering between anti-ferromagnetism to ferromagnetism and ultimately alters the refrigeration (magnetic) capacity of EuTiO3 compound. This work proposes extreme learning machine (ELM) and random forest regression (RFR) computational approaches to model refrigeration capacity of europium titanate magnetic refrigerant with the aid of applied magnetic field, ionic concentrations and ionic radii predictors. Sigmoid activation based extreme learning machine algorithm(SGA-ELM) demonstrates superior performance over sine function activation based extreme learning machine algorithm(SIA-ELM) with improvement of 0.03 %, 16.04 % and 35.03 % on the basis of Pearson correlation coefficient (CC), root mean square error (RMSE) and mean absolute error (MAE) performance yardsticks, respectively on testing set of EuTiO3 based magnetocaloric compounds. SGA-ELM performs better than SIA-ELM and RFR model with improvement of 35.03 % and 66.84 %, respectively while SIA-ELM model performs better than RFR model with improvement of 59.97 %. SGA-ELM model developed in this work outperforms the existing swarm based support vector regression (SW-SVR) models with performance improvement of 15.38 % (for SW-SVR-Gas with Gaussian function) and 42. 71 % % (for SW-SVR-Pol with polynomial function) using mean absolute deviation (MAD) performance yardstick. Significance of magnetic field and dopants concentrations on refrigeration capacity of different EuTiO3 perovskite was investigated with SGA-ELM model. The accuracy of developed models in determining refrigeration capacity of EuTiO3 compounds would ultimately circumvents experimental challenges and explores future potentials of these classes of compound for addressing present and future energy crisis in cooling industries.

Modeling of magnetocaloric effect in RE2X2Y ternary compounds for cooling technology using hybrid intelligent learning computational methods

Cooling technology that employs magnetocaloric effect of magnetic refrigerants has been lately recognized as a green system with immense potential to meet the global refrigeration demand. Hugeness of magnetocaloric impact is one of the major factors which determines the choice of magnetic refrigerants for cooling applications. This research endeavour explores and models magnetocaloric effect (MCE) in ternary system of compounds which combine rare-earth metals (RE = Tm, Er, Ho, Dy, Tb and Gd) due to the localized f-orbital electrons, transition metals (X = Co, Cu and Ni) due to their ferromagnetism near room temperature and other metals (Y = Al, Ga, In, Cd and Sn) using genetic algorithm hybridized support vector regression (GSVR) computational approach. The results of Gaussian transformation function-based hybrid model (G-MCE-GSVR) and polynomial transformation function-based hybrid model (P-MCE-GSVR) are compared with stepwise regression-based model (SR-MCE) using different performance metrics such as mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE). G-MCE-GSVR model outperforms P-MCE-GSVR model using testing data samples with improvement of 66.93 % for RMSE metric, 5.69 % for CC metric and 68.58 % for MAE metric. Similarly, the P-MCE-GSVR model performs better than SR-MCE with improvements of 60.36 %, 37.63 % and 63.41 % corresponding to performance metrics RMSE, CC and MAE, respectively. G-MCE-GSVR model was also employed for investigating the influence of applied magnetic field on magnetocaloric effect in system of RE2X2Y ternary materials. The outstanding performance demonstrated by the developed models coupled with the obtained huge magnetocaloric effect would be of great significance in strengthening practical implementation of magnetic cooling technology for addressing energy crisis and global refrigeration demand.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Triangular Fuzzy Cognitive Maps Method for Modelling Interrelation between Causal and Trigger Factors Towards Students' Mathematics Problem-Solving Ability

The purpose of this study is to model the interrelation between causal and trigger factors towards students' mathematics problem-solving ability by using the triangular fuzzy cognitive maps (TrFCM) method. Selection weaknesses and limitations in the method of relational analysis cause the interrelation and influence between variables not to be visualised and do not reveal the characteristics of the actual interaction. As a result, this study demonstrates TrFCM as a more effective way of analysing the relationship between variables based on the complexity that happens in analysing causal factors and triggers for students' problem-solving abilities in mathematics. The results of the influential relations map (IRM) demonstrate that emotion and metacognition are the triggers for problem-solving ability. While executive function is the main cause of success in completing mathematics problems, it is also influenced by additional factors such as motivation, attention, and working memory. These causal and triggering factors also mobilise parts of students' cognitive and behavioural performance to improve the process of solving mathematics problems. Based on the outcomes of this study, computational intelligence methods like fuzzy systems give useful procedures for analysing data from expert surveys. The TrFCM method offers a more accurate relational analysis procedure in modelling interrelation between human factors.

A novel approach for APT attack detection based on an advanced computing

To enhance the effectiveness of the Advanced Persistent Threat (APT) detection process, this research proposes a new approach to build and analyze the behavior profiles of APT attacks in network traffic. To achieve this goal, this study carries out two main objectives, including (i) building the behavior profile of APT IP in network traffic using a new intelligent computation method; (ii) analyzing and evaluating the behavior profile of APT IP based on a deep graph network. Specifically, to build the behavior profile of APT IP, this article describes using a combination of two different data mining methods: Bidirectional Long Short-Term Memory (Bi) and Attention (A). Based on the obtained behavior profile, the Dynamic Graph Convolutional Neural Network (DGCNN) is proposed to extract the characteristics of APT IP and classify them. With the flexible combination of different components in the model, the important information and behavior of APT attacks are demonstrated, not only enhancing the accuracy of detecting attack campaigns but also reducing false predictions. The experimental results in the paper show that the method proposed in this study has brought better results than other approaches on all measurements. In particular, the accuracy of APT attack prediction results (Precision) reached from 84 to 91%, higher than other studies of over 7%. These experimental results have proven that the proposed BiADG model for detecting APT attacks in this study is proper and reasonable. In addition, those experimental results have not only proven the effectiveness and superiority of the proposed method in detecting APT attacks but have also opened up a new approach for other cyber-attack detections such as distributed denial of service, botnets, malware, phishing, etc.

Intelligent Computational Methods for Optimal Distribution of Friction Dampers in Seismic Protection of Buildings

Intelligent Computational Methods for Optimal Distribution of Friction Dampers in Seismic Protection of Buildings

An intelligent computational iBCMO-DNN algorithm for stochastic thermal buckling analysis of functionally graded porous microplates using modified strain gradient theory

The authors propose an intelligent computational method using the deep feedforward neural network integrated with an improved balance composite motion optimization algorithm to formulate a so-called iBCMO-DNN for solving the stochastic thermal buckling problems of functionally graded porous microplates. In the present approach, the deterministic behaviors of the functionally graded porous microplates were firstly analyzed by the combination of a unified higher-order shear deformation theory, modified strain gradient theory, and Ritz-type series solutions, and then stochastic responses under uncertainty of material properties are obtained by the iBCMO-DNN algorithm. The deep neural network with the long short-term memory model is used as a surrogate method to replace the time-consuming computational model, while the improved balance composite motion optimization is used to search for the optimal solutions. The obtained numerical results for various boundary conditions, uncertainty parameters, and three types of temperature distribution indicated that the proposed method achieves its accuracy and effectiveness in predicting stochastic thermal buckling of the functionally graded porous microplates. The improved balance composite motion optimization algorithm demonstrates a computational time ∼1.7 times faster than its predecessor. Furthermore, integrating a deep neural network into the improved algorithm reduces computational time to about 2/5 compared to the method without it.

Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

Knowledge of the liquid–gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time–frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water–air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metalplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water–air flow regimes (plug, slug, bubble, and transitional plug–bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases.

Intelligent Computational Methods for Damage Detection of Laminated Composite Structures for Mobility Applications: A Comprehensive Review

Intelligent Computational Methods for Damage Detection of Laminated Composite Structures for Mobility Applications: A Comprehensive Review

A Comparative Analysis of Computational Intelligence Methods for Autonomous Navigation of Smart Ships

This paper presents the author’s approaches based on computational intelligence methods for application in the Autonomous Navigation System (ANS) of a smart ship. The considered task is collision avoidance, which is one of the vital functions of the ANS. The proposed methods, applying the Ant Colony Optimization and the Firefly Algorithm, were compared with other artificial intelligence approaches introduced in the recent literature, e.g., evolutionary algorithms and machine learning. The advantages and disadvantages of different algorithms are formulated. Results of simulation experiments carried out with the use of the developed algorithms are presented and discussed. Future trends and challenges of presented smart technologies are also stated.

Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods.

Tailoring Evolutionary Algorithms to Solve the Multiobjective Location-Routing Problem for Biomass Waste Collection

Location-routing problems widely exist in logistics activities. For the biomass waste collection, there is a recognized need for novel models to locate the collection facilities and plan the vehicle routes. So far most location-routing models fall into the cost-driven-only category. However, comprehensive objectives are required in the specific context, such as time-dependent pollution and speed-and load-related emission. Furthermore, location-routing problems are hierarchical by nature, containing the facility location problems (strategic level) and the vehicle routing problems (tactical level). Existing studies in this field usually adopt computational intelligence methods directly without decomposing the problem. This can be inefficient especially when multiple objectives are applied. Motivated by these, we develop a novel multi-objective optimization model for the location-routing problem for biomass waste collection. To solve this model, we explore the way to tailor evolutionary algorithms to the hierarchical structure. We develop adapted versions of two commonly used evolutionary algorithms: the genetic algorithm and the ant colony optimization algorithm. For the genetic algorithm, we divide the population by the strategic level decisions, so that each subpopulation has a fixed location plan, breaking the location-routing problem down into many multi-depot vehicle routing problems. For the ant colony optimization, we use an additional pheromone vector to track the good decisions on the location level, and segregate the pheromones related to different satellite depots to avoid misleading information. Thus, the problem degenerates into vehicle routing problem. Experimental results show that our proposed methods have better performances on the location routing problem for biomass waste collection.

Interval type-2 possibilistic picture C-means clustering incorporating local information for noisy image segmentation

Picture fuzzy C-means clustering is a novel computational intelligence method that has some advantages over fuzzy clustering in pattern analysis and machine intelligence. However, picture fuzzy clustering is easily affected by noise and weighting exponent, which seriously limits its widespread application. To address this issue, this paper proposes a new robust possibilistic clustering method called “interval type-2 possibilistic picture C-means clustering with local information”. This method combines interval type-2 fuzzy sets with possibilistic C-means clustering based on picture fuzzy sets, strengthening the noise resistance of picture fuzzy clustering. Firstly, this paper creatively extends an improved possibilistic clustering with double weighing exponents to picture fuzzy sets, solving the problem of consistency clustering in existing possibilistic picture clustering. Second, this paper originally introduces a new picture local information factor in possibilistic picture clustering and further enhances the anti-noise robustness of the method by using spatial possibilistic picture partition information. Finally, this paper skillfully extends this clustering method to interval type-2 fuzzy sets, which can handle more flexibly high-order uncertainties than type-1 clustering method. Experimental results indicate that this proposed method has better segmentation performance and stronger noise suppression ability compared with existing picture fuzzy clustering and interval type-2 fuzzy clustering. In summary, this work has made significant contributions to the development of picture fuzzy clustering theory and its applications.

Modeling magnetic refrigeration capacity of doped EuTiO3 magnetocaloric compounds using swarm based intelligent computational method

This work models the magnetic refrigeration capacity (MRC) of doped europium titanate (EuTiO3) at different applied magnetic field using hybrid particle swarm optimization based support vector regression (SW-SVR) algorithm with ionic radii dopant descriptors. The developed model with Gaussian function (SW-SVR-Gas) shows better performance as compared with polynomial based model (SW-SVR-Pol) with improvement of 35.76 %, 0.13 % and 45.76 % using mean absolute error (MAE), correlation coefficient (CC) and root mean square error (RMSE) performance yardsticks, respectively. The developed SW-SVR-Gas model investigates the behavior of Eu1-xBaxTiO3 and EuTi1-xNixO3 doped europium titanate at different dopants concentration as well as applied field. Novelties of the developed model include circumvention of experimental difficulties associated with magnetic refrigeration capacity determination, implementation of simple molecular descriptors with high precision and accuracy. The demonstrated uniqueness of the developed model would strengthen exploration of refrigeration capacity of europium titanate based compounds for addressing global refrigeration crisis.

Argumentation and discourse analysis in the future intelligent systems of essay grading

Intelligent systems of essay grading constitute important tools for educational technologies. They can significantly replace the manual scoring efforts and provide instructional feedback as well. These systems typically include two main parts: feature extractor and automatic grading model. The latter is generally based on computational and artificial intelligent methods. In this work, we focus on the features extraction part. More precisely, we focus on argumentation and discourse related-features, which constitute high level features. We discuss some state-of-the-art systems and analyse how argumentation and discourse analysis are used for extracting features and providing feedback.

Ensemble machine learning reveals key features for diabetes duration from electronic health records.

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.