Swarm Intelligence Models Research Articles

Hybrid Bee Colony Algorithm with Whale Algorithm

In this research paper, two hybrid algorithms of Meta-Heuristic algorithms, both of which are inspired by nature, are presented: the Bee- settlement Amendment algorithm (BCO) and the Whale Amendment Algorithm (WOA). The Bee settlement Amendment algorithm is an amendment algorithm founded on swarm intelligence modeling attitude. It is one of the techniques of synthetic information that focuses on studying the grouping conduct of decentralized systems, which are represented by groups of modest elements that react locally simultaneously and with the surrounding perimeter. One of the methods that distinguishes it is the method of exploration. As for the Whale Amendment Algorithm, whom represents the friendly conduct of the hump-back whale. It is based on the fancy net fishing design. One of the advantages of this algorithm depends on the poise between screening and utilization and avoidance of falling into local solutions. A hybridization process was carried out between the two algorithms, and the new algorithm was named (ABCWOA), and the whole algorithm was used to 16 rising -measurement Amendment assignment with diverse frequencies between (100, 200, 500, and 1000). The outcomes of the proposition algorithm showed access to optimality solutions by achieving the minimum value (f_min). For most assignment, the outcomes of this algorithm were disparity with the search algorithms.

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

Multimethod computing technology for searching energy-optimal motion modes of electric rolling stock of railways

The article is devoted to the problem of finding energy-optimal modes of movement of electric rolling stock of railways, considered as a set of problems of finding a global extremum of a functional defined on a set of logical-dynamic system (LDS) movement trajectories that are acceptable under operating conditions, and a structural-parametric synthesis of the LDS control strategy and optimization of its parameters based on random search methods, swarm intelligence, evolutionary algorithms and multi-agent models. A multi-method technology for optimizing the modes of train movement according to the criterion of minimum energy consumption, and software and algorithmic support based on the original multi-agent evolutionary-swarm method for synthesizing the optimal-terminal control of multi-mode moving objects developed by the author are proposed. The specified computing technology includes the following stages: formation of a set of promising control strategies; their optimization and recombination based on the evolutionary genetic algorithm; finding the global extremum of the functional using parametric optimization of the solutions identified at the previous stages using the modified particle swarm method. The computational experiments performed with the simulation model of train movement showed that when a sufficiently large number of options for control strategies are generated at the first stage, the algorithm converges to the global extremum of the functional.

A survey of inspiring swarm intelligence models for the design of a swarm-based ontology for addressing the cyber security problem

The increased use of the internet raises concerns about the security of data and other resources shared in cyberspace. Although efforts to improve data security are visible, the need to continuously explore other avenues for preventing and mitigating cyberattacks is apparent. Swarm intelligence models have, in the past, been considered in cybersecurity though there was no formal representation of the swarm intelligence knowledge domain that defines how these models fit into the cybersecurity body of knowledge. This article reviews the aspects of three swarm intelligence models that may inspire the design of the desired swarm intelligence ontology. The algorithms are particle swarm optimization, ant colony optimization, and the artificial bee colony model. In each case, we investigate the main driving features of the model, the causal aspects, and the effects of those causal aspects on the resolution of the cybersecurity problem. We also investigate how these features can be recommended as the building blocks of the desired swarm intelligence ontology. Investigations indicate that the artificial bee colony model has three outstanding aspects considered for the design of the swarm intelligence ontology and that is the quality, popularity, and communication. Foraging through pheromone deposits is an outstanding component of ant colony optimization that aids in locating threats sources more quickly by using the shortest route or tracks with high pheromone deposits. The particle swarm optimization model, on the other hand, adds alignment, cohesion, and collision avoidance aspects to the ontology to augment the ant colony and artificial bee colony algorithms. In our view, although intrusion detection is a complex problem in cybersecurity, the power of integrated swarm intelligence models is more than the sum of the individual capabilities of each swarm intelligence model individually. The article, therefore, proposes a swarm intelligence ontology that will potentially bring us closer to resolving the general cybersecurity problem.

Research on a bionic swarm intelligence algorithm and model construction of the integrated dispatching system for the rescue of disaster victims

Research on a bionic swarm intelligence algorithm and model construction of the integrated dispatching system for the rescue of disaster victims

Research on a bionic swarm intelligence algorithm and model construction of the integrated dispatching system for the rescue of disaster victims

Research on a bionic swarm intelligence algorithm and model construction of the integrated dispatching system for the rescue of disaster victims

Hybrid optimization algorithm based on the combination of swarm intelligence models: a swarm of chromosomes and bees

A hybrid algorithm for solving the problem of placing VLSI elements has been developed, which combines the procedures for the behavior of a group of bees and a swarm of chromosomes. The optimization criterion is based on estimates of the number of nets crossing the given lines of the routing area. A modified chromosome swarm paradigm is described, which, in contrast to the canonical method, provides the possibility of searching for solutions in an affine position space with integer parameter values. As the weight of the affine connection between chromosomes on the affine-relaxation model of a swarm of chromosomes, estimates of the degree of «difference» between them are used. A modified structure of the bee algorithm is proposed. For each base position, a probabilistic choice of a set of positions located in its vicinity is implemented. The time complexity of the algorithm is О(n2) – О(n3).

Compatible Swarm Intelligence Model and Construction Method to Satisfy Bridge Design and Load Rating Requirements and Considering Geotechnical Variability

This paper details the novel combination of a population-based swarm intelligence algorithm, structure mechanics, geotechnical variability, and a specific construction staging methodology, to propose an autonomous construction system which can erect a bridge over arbitrary crossing geometry while satisfying project constraints and conventional design code requirements (the Proposal). The fields of metaheuristic applications in structures (MAS), unmanned aerial systems (UAS), and additive manufacturing are all independent innovations in the transportation industry. The Proposal brings these fields together, with a vision that independent drone robots with very basic programming could construct a bridge with no user interruption and without performing a conventional structural design. A simulation as proof-of-concept is compared with a modern bridge design; the result is a modest increase in materials with an anticipated dramatic saving in labor. The paper also studies the effects of geotechnical variability for insight into preparation for possible future construction methodologies and algorithms. The Proposal has promise where socio-economic issues may be a factor, such as construction of infrastructure in remote locations or developing nations, and emergency repair or replacement of bridges. With recent U.S. policy supporting a permanent lunar presence and expanding Moon to Mars exploration through the Artemis program, the Proposal may also assist infrastructure development in advance of human missions.

Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach

Nowadays, Artificial Intelligence (AI) based modeling is the major consideration to build efficient, automated, and smart systems for our today's needs. Many companies are benefited from these modeling methods for their marketing efforts. Each firm has expected to increase its product development in an innovative way to improve its business growth. Successful firm marketing is to offer the right product to the right person at the right time. To market the product to the customer successfully, it is needed to segment the customer by finding their behavioral patterns. The customer behaviors and their purchasing patterns are used to generate profit for the company. Customer segmentation is the process of grouping customers based on commonalities. Developing an efficient AI-based customer segmentation to improve digital marketing growth is a challenging task. In this paper, an unsupervised deep learning model called a Self-organizing map with an Improved social spider optimization approach has been used for efficient customer segmentation. The customer data are analyzed by a feature engineering process using a swarm intelligence model called Modified social spider optimization to select the behavioral features of the customer. Then, the customers are clustered using Self Organizing neural network (SONN). Based on the clusters, the customers are classified using the Deep neural network (DNN) model. The experimental results prove the performance of the proposed model with high clustering and segmentation capability to improve the business profit in marketing.

An optimization-based selection approach of landing sites for swarm unmanned aerial vehicles in unknown environments

Autonomous selection of landing sites is an important capability of a mission when unmanned aerial vehicles (UAVs) face technical difficulties or adverse weather, especially in unknown environments. In order to build a better decision-making system for swarm UAVs landing, besides terrain safety, the global flight cost among individuals should be also considered during descent. However, the existing methods cannot solve these problems elegantly due to terrain uncertainty and optimization complexity. In this paper, we present an optimization method to tackle this issue based on point cloud integrating the coarse-to-fine manner and swarm intelligence model. Specifically, our method starts with a point cloud preprocessing module for sparse elevation estimation and robust path planning. In the coarse stage, a novel cost map is constructed by extracting only low-level features from the elevation map. Based on the optimum results of the coarse selection, the final landing sites are automatically generated and finely evaluated in terms of multiple 3D terrain factors. Finally, the landing model of swarm-UAVs is treated as an unbalanced assignment problem, which minimizes flight costs by incorporating terrain safety, fuel consumption, and path planning. Experimental evaluations on three different real-world scenario datasets demonstrate the effectiveness of our method in both normal operations and emergency situations.

ПОРІВНЯЛЬНИЙ АНАЛІЗ ТА СТВОРЕННЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ ДЛЯ ФОРМУВАННЯ МАКЕТА РОЙОВОЇ ВЗАЄМОДІЇ

The subject of study is the process of creating an unmanned aerial vehicle of the helicopter type, which is able to perform autonomous flight and be used in the premises to test the algorithms of swarm interaction. The aim is to reduce the cost of forming and operating the UAV swarm intelligence model, ensuring compatibility with existing hardware positioning systems and simulation software platforms. Tasks: to analyze the existing prototypes of unmanned aerial vehicles of the helicopter type, used for modeling and testing of autonomous flight modes indoors; to review the systems of positioning and exchange of information between the participants of the swarm formation and to analyze the hardware systems necessary for the implementation of these systems; to develop the architecture of hardware subsystems for unmanned aerial vehicles capable of performing tasks at the level of the considered prototypes; design a case for a sound hardware platform, check its operational parameters, implement its collection and testing; install software that allows you to check the operability of the considered platform of the unmanned aerial vehicle. The methods used are: systems analysis used to compare models of unmanned aerial vehicles within the tasks, methods of analysis and synthesis of schemes for the implementation of the hardware platform, the method of graphic modeling for the design of the unmanned aerial vehicle, methods of system programming for flight program. The following results were obtained. The choice of the hardware platform of the unmanned aerial vehicle is substantiated; the hull for the helicopter was developed and implemented, two platforms of unmanned aerial vehicles were collected and tested during autonomous flight indoors. Conclusions. The study found that there are existing and widely used platforms for compatible indoor flight. These platforms are open and closed. The analysis of open hardware platforms and synthesis of architecture of own unmanned aerial vehicle is made. The designed and implemented unmanned aerial vehicle is capable of autonomous flight, has all the necessary components to perform a compatible flight program. As part of the layout of swarm interaction, it is necessary to further adjust the positioning system.

Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model

In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step.

Model Selecting PSO-FA Hybrid for Complex Function Optimization

Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

A Survey of Computational Offloading in Cloud/Edge-based Architectures: Strategies, Optimization Models and Challenges

In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

Swarm intelligence‐based optimal device deployment in heterogeneous Internet of Things networks for wind farm application

SummaryInternet of Things (IoT) is becoming a suitable surveillance system in modern power grid, right from power generation to distribution. In the case of wind power generation application, the entire wind farm is considered completely monitored using edge devices known as IoT devices. Device deployment is a major obstacle that can be addressed for this application as unfair device deployment can lead to connectivity issue. This study has two main objectives. The first objective is to determine the optimal number of IoT devices that need to be place in the potential key elements of the wind turbines/targets for coverage assurance. The second objective is to determine the optimal number of relay devices. Spacing wind turbines several hundred meters apart brings the connectivity as a second critical issue, which can be handling by placing optimal number of relay devices. Hence, the focus of this study is to address connected target coverage problem in optimal device placement of heterogeneous IoT network for wind farm monitoring. This paper discusses in detail the swarm intelligence technique known as particle swarm optimization (PSO) to solve the connectivity issue and to minimize the cost of devices deployment. Additionally, a hybrid swarm intelligence model called hybrid PSO‐ACO (HPACO) algorithm is proposed. The main goal of this hybrid model is to reduce the number of relay devices, minimize deployment cost, and to improve network connectivity. The performance of both PSO and HPACO algorithms are validating by comparing the extensive simulation results with ant colony optimizations (ACO) algorithm and other approximation algorithm.

Swarm intelligence, social force and multi-agent modeling of heroic altruism behaviors under collective risks

Terrorists usually choose to attack soft targets, with low self-protection abilities and resistance strengths, such as schools, campus, public squares, railway stations, etc. Under great panic, civilians attacked tend to escape aimlessly and disorderly. Hence, colliding, pushing, and trampling will take place and lead to indirect deaths and injuries. If some heroes with the spirit of altruism, coming from civilians, stand up and fight terrorists bravely, the injuries and deaths will be greatly reduced. Agent-based modeling is built to explore the role of heroes during terrorist attacks. The particle system of three categories of agents, civilians, terrorists & heroes, is built to simulate the Peshawar School Case in 2014. Multiple action rules and mechanisms are introduced, such as swarm intelligence, information communication, self-organized behaviors, and heroic behaviors. We run each simulation repeatedly for 100 times to obtain averaged (robust) outcomes. The optimal combination of parameters, which best matches real outcomes, will be solved accordingly. It indicates that: (a) the self-organizing of crowd behaviors greatly improves the survival rate of civilians. Hence, well-planned trainings of counter-terrorists emergence responses can enhance capabilities of civilians; (b) people should learn from birds’ swarm behavior in crowd evacuations. As a swarm intelligence pathway, small groups and information sharing during crowd escape simulations can greatly improve survival rates; and (c) the society should encourage more people to be altruistic heroes who are acting prosocially. Besides, it suggests that more heroes bring safer overall outcomes for civilians and even for heroes themselves.

Размещение элементов СБИС на основе моделей роевого интеллекта

Размещение элементов СБИС на основе моделей роевого интеллекта

A swarm intelligence modeling approach reveals noble gas cluster configurations confined within carbon nanotubes.

Confinement of atoms and molecules brings forth fascinating properties to chemical systems that are otherwise not known in the bulk. Carbon nanotubes (CNTs) and fullerenes are excellent hosts for probing the confinement effects. Herein, we explore the potential energy surfaces of large noble gas clusters, Ngn (Ng = He, Ne and Ar; n = 10, 20, 30, 40, and 50), in the confines of CNTs of various lengths. Our implementation involves integrating the continuum approximation for CNTs with the well-known swarm intelligence technique, particle swarm optimization (PSO), followed by a deterministic local optimization. Global search techniques such as PSO have been increasingly utilized in recent times to track down minimum energy configurations on highly rugged potential energy surfaces. Aside from the position vectors of the noble gas atoms, we have considered the radius of the CNTs as a design variable. Such an approach enabled us to predict the optimal CNT radii for the encapsulation of each of the clusters. Confined cluster geometries ranging from linear, zig-zag, and double-helical to spiral configurations are obtained on encapsulation, in sharp contrast to their bare cluster geometries. On increasing the CNT length, our approach yielded quasi-linear geometries, suggesting that the length of the CNTs plays a crucial role in determining the stable cluster configurations on confinement.

Solving the problem of placing the VLSI elements based on the integrating of swarm intelligence models into the affine search spaces

Solving the problem of placing the VLSI elements based on the integrating of swarm intelligence models into the affine search spaces

Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping

This study proposed and compared several novel hybrid models that combined swarm intelligence algorithms and Deep Learning Neural Network for flood susceptibility mapping. Lai Chau, a province in the northwest mountainous region of Vietnam was chosen as a case study since it had recently undergone severe flashflood in 2018. For this purpose, numerical predictor variables such as topographically derived factors (Digital Elevation Model, Aspect, Slope, Curvature, Topographic Wetness Index), climatic variables (Rain), and hydrological variables (stream density, stream power index, distance to river) and multiple remote sensing indices (Normalized Difference Vegetation Index, Normalized Difference Buildup Index) were used. These predictor variables were selected because they are globally collectible and reproducible. The performances of these models were evaluated by using common statistical indicators, namely Root Mean Square Error, Mean Absolute Error, Overall Accuracy and Area under Receiving Operating Characteristics, and the statistical test of differences. The results showed that the proposed swarm intelligence models outperformed benchmarked methods, namely Particle Swarm Optimization, Support Vector Machine, Random Forest in almost all comparing indicators. It is suggested that proposed models are more robust than the classifiers, which were used for benchmarking and they are good alternatives for flood susceptibility mapping given the availability of dataset.