Swarm Intelligence Research Articles

Constructed encoded data based coded distributed DNN training for edge computing scenario

Deep learning in unmanned aerial vehicle (UAV) deployment encounters two problems: 1, One UAV may fail to store and execute large Deep Neural Network (DNN) model. 2, One UAV fails to accomplish real time services. One possible solution is the group of UAVs (nodes) collectively generate swarm intelligence in the form of edge computing scenario, namely in the distributed computing (DC) mode with clever arrangement, say Coded Distributed Computing (CDC). In CDC systems, the redundant computation introduced by linear coding can compensate stragglers. However, since linear property cannot pass the nonlinear activation function in Deep Neural Network (DNN) training, coding/decoding for CDC need to be applied layer by layer, which slows down the training. To avoid layer-by-layer coding/decoding, we propose a novel DNN training scheme based on constructing encoded data. This construction process lies before the training process (can be done before training without any impact on training efficiency). Based on both the original data and the newly constructed encoded data, the training phase can take advantage of the (n,k) property (Wait for the first k returned data) and hence improve the training speed. The training process does not require encoding/decoding operations, and hence significantly improves the training speed. Experimental results show that the training scheme based on constructed encoded data can achieve prediction accuracy approximating that of the centralized one and significantly reduce the latency compared to the layer-by-layer linear encoding and decoding scheme.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

Проблема ценности в цифровом обществе роевого интеллекта

Introduction. The emerging digital society demonstrates the problem of justifying the value, worth and price of digital products and services, which requires consideration of the specificity of value in this society. Theoretical analysis. Following the financial and art market, the boundary between “value” and “worth” is blurred in the digital society. Value, including the value of an individual, becomes variable and is conditioned by the presence of unique attributes and unique experiences that obtain market value. At the same time, under standard digital protocols and technologies, the true uniqueness of an individual experience can only be achieved through the creation of customized digital ecosystems in the form of robotic swarms that support human life activities. A set of digital devices that have sufficient resources to continuously self-renew and form a closed environment around the individual that provides a unique experience can be called a chrysalic swarm. The chrysalic swarm creates an opportunity for the individual to escape from the logic of mutual competition dictated by society. Conclusion. Studying the value problematization in the digital society of swarm intelligence opens a theoretical perspective on the problem of the crisis of social elites in the context of the fulfillment of this scenario of society development.

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.

Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations

A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulation results show that the approach works in delicate environments including warehouses, laboratories, and other indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.

Exploring advancements and emerging trends in robotic swarm coordination and control of swarm flying robots: A review

Swarm Robotics (SR) is an interdisciplinary field that is rapidly advancing to address complex industrial challenges. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of Swarm Flying Robots (SFRs). The motivation behind this review is to explore scalable and robust solutions for SFRs to enhance their performance and adaptability across various applications. Key objectives include examining the characteristics and essential behaviors of SR, analyzing the challenges and so lutions for implementing SR in Flying Robots (FRs), and highlighting current and future research directions. The review delves into critical areas such as multiple robot path planning, Swarm Intelligence (SI), combinatorial optimization, and formation flying using SFR. Special attention is given to coordination and control techniques, including formation control in GPS-denied environments, to underscore their significance in advancing SR. The paper also addresses ethical, privacy, and security considerations, emphasizing the importance of responsible practices in SR development. Major takeaways from this review include the identification of key technical challenges and potential solutions in SFR, the exploration of SI algorithms, and the future research directions necessary for fully realizing the potential of SR technologies. By offering detailed insights into state-of-the-art research and its industrial implications, this paper serves as a foundational guide for future studies in the dynamic and promising domain of swarm robotics.

Optimizing Deep Learning Models with Improved BWO for TEC Prediction.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence

The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimization techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches.

Structural optimization design and compression performance of porous titanium prepared by laser powder bed fusion

Porous implants composed of periodically-repeating porous units have been applied in orthopedic surgeries to replace damaged bones. An important requirement for bone implants is to find a balance between biological and mechanical properties, that is, to ensure high pore connectivity and a certain strength. In this work, center-supported and perimeter-supported porous optimization models were established by a swarm intelligence optimization algorithm, and the corresponding titanium porous samples were fabricated by laser powder bed fusion (L-PBF). The effects of cell topology and porosity on the compressive behavior of L-PBF titanium porous structures were systematically investigated by a combination of compression simulation and experiments. Compression simulation shows that the deformation failure behavior of center-supported and perimeter-supported porous structures consists of three stages: elastic deformation, plastic deformation and compression collapse, showing the characteristics of elastic-plastic porous materials. According to the compression test results, the compressive strength of center-supported and perimeter-supported structure samples are 151–188 MPa and 178–196 MPa, respectively, and the compressive stability of the perimeter-supported structure is better than that of the center-supported structure. The porosity error of center-supported structure is 1.6%–2.9%, and the porosity error of perimeter-supported structure is 3.1%–4.5%. The compressive strength of the same type of structure decreases with the increase of the porosity of the porous structure. The simulation/experimental compressive strength errors of center-supported and perimeter-supported structures are less than 6% and 5.9%, respectively. The constructed quasi-static compression model has certain guiding significance for the study of the fracture of the titanium implants under external force.

Localizing particulate matter sources in indoor environments with weak airflow: An experimental study using swarm intelligence methods

In indoor environments with weak airflow, advective transport diminishes while turbulent dispersion prevails. The absence of usable airflow data and the unpredictability of turbulence-led pollutant dispersion pose significant challenges to mobile robots in localizing pollution sources. This study advances the field by shifting the focus from gases to particulate matter (PM) in such environments. An initial detailed examination of airflow dynamics and PM behavior under weak airflow conditions laid the foundation for extensive 3D source localization experiments using two methodologies: the Whale Optimization Algorithm (WOA_3D) and Particle Swarm Optimization (PSO_3D), executed through a specialized multi-robot system. Evaluation across 11 distinct scenarios and 165 trials showcased the methods’ adaptability to different source heights, PM sizes, and the transition from gaseous to particulate pollutants. WOA_3D notably excelled in localizing PM2.5 sources at 1.35 m heights, achieving a 73.3% success rate and proving consistently effective across various elevations. However, its efficacy waned with larger PM sizes, and it was less effective in transitioning from ethanol vapor to PM source localization. These results highlight the importance of addressing PM settling to improve PM localization strategies, effectively combining insights into PM behavior’s complexities with methodological progress.

PARALLEL PLATFORM CONTROLLER BASED ON ADAPTIVE DIFFERENCE ALGORITHM – PART 2

There are two main approaches to motion control on parallel platforms: joint space control and workspace control. Joint space control is an easy-to-implement semi-closed-loop strategy, but its control effect could be better. The workspace control is to obtain the real-time position of the parallel platform through the forward solution and close the speed and position loop of the parallel platform in the workspace. This paper uses a Model Predictive Controller (MPC) to control the parallel platform with workspace control as the research goal. The loss function is constructed based on the swarm intelligence optimization idea, and the adaptive difference algorithm is used to optimize the parameters of MPC. This part uses MATLAB to perform simulation experiments to complete the S-shaped velocity trajectory planning algorithm. In addition, the control effect of MPC and position-loop PI controller in a robust disturbance environment is compared. Experiments show that MPC has the advantages of low energy consumption and high control accuracy.

Enhancement of heat dissipation rate of a heat sink with perforated fins using CFD and Swarm Intelligence

Enhancement of heat dissipation rate of a heat sink with perforated fins using CFD and Swarm Intelligence

Research on the Capacity Allocation and Planning Model of a Flexible Direct Current Transmission System Based on Swarm Intelligence Optimization Algorithm

Based on the contradiction between the increasing power demand and the low capacity of the traditional direct current (DC) transmission system, this study innovatively combines the swarm intelligence optimization algorithm with power transmission and storage, and establishes a flexible DC transmission system model based on the swarm intelligence optimization algorithm. The study also analyzes the difference between the optimized capacity configuration of flexible DC transmission system and the traditional transmission system under different factors. The research results show that the transmission mode has become an important factor affecting the capacity allocation of flexible DC transmission system. The transmission medium and transmission cable type also have an impact on the capacity allocation of flexible DC transmission system. Optimizing power transmission mode, changing transmission medium and whether to add the swarm intelligence optimization algorithm are of far-reaching significance for improving the capacity allocation of flexible DC transmission system.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

A Path Planning Method Based on Hybrid Sand Cat Swarm Optimization Algorithm of Green Multimodal Transportation

Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning.

Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey Optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization; enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with non-convex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model

To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time–frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving

The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets.

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

DCWPSO: particle swarm optimization with dynamic inertia weight updating and enhanced learning strategies.

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm's validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.