Swarm Intelligence Algorithms Research Articles

SwarmIntelligence-Based Multi-Robotics: A Comprehensive Review

Swarm Intelligence (SI) represents a paradigm shift in artificial intelligence, leveraging the collective behavior of decentralized, self-organized systems to solve complex problems. This study provides a comprehensive review of SI, focusing on its application to multi-robot systems. We explore foundational concepts, diverse SI algorithms, and their practical implementations by synthesizing insights from various reputable sources. The review highlights how principles derived from natural swarms, such as those of ants, bees, and birds, can be harnessed to enhance the efficiency, robustness, and scalability of multi-robot systems. We explore key advancements, ongoing challenges, and potential future directions. Through this extensive examination, we aim to provide a foundational understanding and a detailed taxonomy of SI research, paving the way for further innovation and development in theoretical and applied contexts.

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.

An Improved Ant Colony Clustering Algorithm and Application in Classification of Tree Species

Abstract Clustering is one of the most challenging research topics and has attracted the attention of many researchers. Clustering aims to divide a given set of objects into several categories according to certain rules. It is widely applied to practical problems, such as knowledge discovery, artificial intelligence, scientific data analysis, business, biology, medical diagnosis, text mining, and web data mining and so on. Ant colony clustering algorithm (ACCA), which integrates ACA into clustering, is an efficient swarm intelligence algorithm. There are some weakness in the basic ant colony clustering algorithm, such as low solving efficiency, poor calculation accuracy and iteration stagnation. In this article, an improved Ant Colony Clustering Algorithm (IACCA), in which novel picking-up and dropping strategy is introduced, is proposed and applied to classification of tree species. The experiments indicate efficiency of IACCA for classification of tree species.

Research on Polyurethane Creep Prediction Model Based on BWO-BP

Abstract Polyurethane foam materials can creep under the influence of temperature, humidity, and pressure during use, which seriously affects their safety and state of use. Therefore, studying the creep behavior of polyurethane foam materials and developing corresponding creep mechanism models have significant scientific value for understanding their damage patterns. As a type of machine learning, Backpropagation (BP) neural network can be used for data analysis. However, there are many issues when determining the network structure. Whale Optimization (BWO) algorithm, as a type of swarm intelligence algorithm, can efficiently determine the framework of the BP model. BP which has been optimized through the BWO algorithm, has been utilized to construct a model for the prediction of polyurethane’s creep failure. The optimization process has significantly lowered the mean squared error from 0.30 in the non-optimized BP model to 0.07 in the enhanced BWO-BP model, which validates the effectiveness of the proposed algorithm.

РОЗРОБКА АДАПТИВНИХ АЛГОРИТМІВ РОЄВОГО ІНТЕЛЕКТУ У ПРОЕКТУВАННІ ТА УПРАВЛІННІ ІНЖЕНЕРНИМИ СИСТЕМАМИ

The article examines the design and management of engineering systems in the context of growing data volumes in local computer networks. With the development of swarm intelligence, the probability of the existence of the necessary information for managing engineering systems increases, but the possibility of finding it decreases. This creates the need to develop new methods and tools for using adaptive algorithms of swarm intelligence. The purpose of such algorithms is to ensure the ability to process information and find queries with maximum relevance, generating the largest number of search results (SERPs) containing technical information. The development of swarm intelligence algorithms is impossible without their intellectualization, which includes semantic and syntactic analysis of texts, natural language tools, and intelligent algorithms for determining the significance of information resources. In digital information retrieval systems (DIRS), a query is formed in the form of keywords or their combinations, connected by logical operations. To search for the same information, different keywords are used, the choice of which is subjective. The first stage of the algorithm consists in determining the set of meaningful correspondences and forming the initial set of requests within the engineering system. To ensure the completeness of the selection, the total relevance of the SERP in relation to the group of significance criteria is considered. There is a need to develop an efficient neural network algorithm to solve the problem of assigning one SERP to each group of query criteria. This will make it possible to distribute SERPs by criteria groups so that each page is evaluated mainly by one criteria group, and the total relevance of all groups is maximized. Thus, the article emphasizes the importance of developing new methods and tools for implementing adaptive swarm intelligence algorithms in engineering systems. Special attention is paid to the development of effective neural network algorithms that will ensure optimal distribution of the relevance of search pages, which will increase the accuracy and efficiency of information search in complex engineering systems.

Multiple-perspective consumer segmentation using improved weighted Fuzzy k-prototypes clustering and swarm intelligence algorithm for fresh apricot market

Leveraging clustering technology for consumer segmentation is crucial for discerning the nuanced differences among fresh apricot consumer groups and subsequently executing precise marketing strategies. To achieve a more comprehensive and lucid consumer segmentation and identify typical characteristics of apricot consumers in different clusters, this research constructs a novel multiple-perspective segmentation indicator system for fresh apricot consumers. Given the diverse degrees of importance and types of consumer segmentation variables, and the inherent sensitivity of the original Fuzzy k-prototypes (FKP) algorithm to clustering centers, we proposed the weighted Fuzzy k-prototypes (WFKP) algorithms for mixed data (MD) optimized by the particle swarm optimization (PSO) algorithm (MDPSO-WFKP) and mixed data sparrow search algorithm (SSA) (MDSSA-WFKP), both incorporating information entropy weighting for mixed attributes. We test the proposed algorithms on four University of California Irvine machine learning repository (UCI) datasets and the consumer segmentation dataset, and the performance of all selected evaluation indexes shows significant improvement. These findings unequivocally validate the efficacy of the proposed methodologies. Since the MDSSA-WFKP algorithm has the best comprehensive effect on the evaluation indexes, we use it to conduct in-depth apricot consumer segmentation research and find that the apricot consumers can be subdivided into three groups with differentiation: ‘Buddhist-like youths’, ‘Upscale attribute enthusiasts’, and ‘Quality-oriented consumers’. Finally, this paper gives the corresponding marketing suggestions based on the characteristics of the segmented groups.

Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable.

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.

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.

Large-scale magnetic target state estimation using model parameter optimization

Abstract Aiming at the problem of non-negligible target scale in the state estimation of magnetic targets, a state estimation method that requires only the measurement data of a single three-axis magnetic sensor and is insensitive to the initial value is proposed. Firstly, the state estimation model of a single three-component magnetic sensor is established based on a magnetic dipole array. A signal preprocessing method is designed to reject useless signals and keep the waveform intact. Five swarm intelligent optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Hummingbird Algorithm (AHA), Artificial Rabbits Optimization (ARO), Grey Wolf Optimizer (GWO), and Improved GWO (IGWO) are introduced to perform state optimization, and two methods, such as Conjugate Gradient Least Squares (CGLS), and Stepwise Regression (SR), are introduced to calculate the magnetic moment parameters, and PSO-CGLS, AHA-CGLS, AHA-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, and ARO-CGLS are designed in combination. CGLS, ARO-CGLS, GWO-CGLS, IGWO-SR, IGWO-CGLS, and six state estimation methods. In order to address the problem that the parameter optimization range is too large, the state to be estimated is adjusted from the initial state to the state at the moment of the maximum value of the magnetic field modulus, and the method of positive transverse distance estimation is used to narrow the range of positive transverse position optimization. Finally, numerical simulation tests and ship model tests are used for verification, and the results show that the proposed methods are accurate and can meet the needs of precise target state estimation.

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.

Inverse kinematics of six degrees of freedom robot manipulator based on improved dung beetle optimizer algorithm

Inverse kinematics is a basic problem in robotics, which aims to solve the robot’s joint angles according to the end effector’s position and orientation. This paper proposed an improved spiral search multi-strategy dung beetle optimizer (DBO) algorithm for solving the inverse kinematics problem. The improved DBO algorithm considers not only the error between the target value and the current value but also the previous position of the robot to ensure minimum displacement during the movement. To solve the end position error and orientation error of the robot end effector more accurately, the quaternion is introduced as a penalty factor in the optimization objective function, which is of great significance for reducing the orientation error. Through the improved DBO algorithm, the position error is still accurate, and the orientation error is reduced from 9.5901 to 1.8718. Experimental results show that the proposed algorithm outperforms other swarm-intelligent algorithms in terms of accuracy and convergence speed. Overall, the proposed spiral search multi-strategy DBO algorithm provides an effective and efficient solution to the inverse kinematics problem in robotics.

Swarm intelligence-based optimization of machine learning models for diverse industrial problems: a comparative study

Abstract With the rapid development of artificial intelligence, machine learning has emerged as a promising approach to tackle complex problems in various industries. It is particularly evident in situations where limited data samples are available, such as when developing new materials, or in high-speed machining scenarios where computational efficiency and result reliability are equally crucial. In this work, we have selected five widely recognized machine learning models (e-SVR, v-SVR, BP, Random Forest, Extreme Learning Machines) as baseline methods. We employ Snake Optimizer, a recently popular swarm intelligence algorithm, to optimize the parameters of each model individually. The Mean Squared Error is utilized as an evaluation fitness measure to adaptively optimize and determine the optimal hyperparameter combination for each method. Consequently, five hybrid models are established. To thoroughly evaluate the performance and applicability of these models, predictive experiments are conducted using temperature data of permanent magnet synchronous motors, energy consumption data from steel companies and real-estate price data of Chicago, representing three distinct scenarios. The learning ability and generalization capability of these models are tested and assessed using four error measures: Mean Absolute Error, R-Squared, Root Mean Squared Error, and Computation Time. Moreover, comparison and discussion between the hybrid models and their respective basic models are also conducted, analysing the effectiveness of SO algorithm in optimization. Through analysis and discussion, a comprehensive understanding of the characteristics and applicability of them is provided. Our objective is to provide scientific researchers and engineers with valuable insights into different methods and their characteristics.

Development of a method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm

The problems of processing heterogeneous data are discontinuous, undifferentiated, and multimodal. The most common approaches to processing heterogeneous data are swarm intelligence algorithms (swarm algorithms). Given the above, classical gradient deterministic algorithms are inappropriate for solving the problems of processing heterogeneous data. The problem solved in the study is to increase the efficiency of processing heterogeneous data circulating in information systems, regardless of the number of data sources. The object of the study is hierarchical systems. A method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm is proposed. The study is based on the reptile algorithm (RA) for processing heterogeneous data circulating in the system. For RA training, evolving artificial neural networks are used. The originality of the proposed method lies in setting RA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining RA feeding locations, which allows prioritizing the search in a given direction. The next element in the originality of the study is the possibility of choosing an RA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each RA. This makes it possible to optimize the speed of exploration of each RA in a certain direction. The method provides a 15–19 % increase in data processing efficiency by using additional improved procedures. The proposed method should be used in processing large amounts of data

FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion.

Adaptive spiral flight and multi-strategy fusion are the foundations of a new FOX optimization algorithm that aims to address the drawbacks of the original method, including weak starting individual ergodicity, low diversity, and an easy way to slip into local optimum. In order to enhance the population, inertial weight is added along with Levy flight and variable spiral strategy once the population is initialized using a tent chaotic map. To begin the process of implementing the method, the fox population position is initialized using the created Tent chaotic map in order to provide more ergodic and varied individual beginning locations. To improve the quality of the solution, the inertial weight is added in the second place. The fox random walk mode is then updated using a variable spiral position updating approach. Subsequently, the algorithm's global and local searches are balanced, and the Levy flying method and greedy approach are incorporated to update the fox location. The enhanced FOX optimization technique is then thoroughly contrasted with various swarm intelligence algorithms using engineering application optimization issues and the CEC2017 benchmark test functions. According to the simulation findings, there have been notable advancements in the convergence speed, accuracy, and stability, as well as the jumping out of the local optimum, of the upgraded FOX optimization algorithm.

Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications.

The dung beetle optimization (DBO) algorithm is acknowledged for its robust optimization capabilities and rapid convergence as an efficient swarm intelligence optimization technique. Nevertheless, DBO, similar to other swarm intelligence algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm in this paper. MEDBO utilizes a good point set strategy for initializing the swarm's initial population, ensuring a more uniform distribution and diminishing the risk of local optima entrapment. Moreover, it incorporates convergence factors and dynamically balances the number of offspring and foraging individuals, prioritizing global exploration initially and local exploration subsequently. This dynamic adjustment not only enhances the search speed but also prevents local optima stagnation. The algorithm's performance was assessed using the CEC2017 benchmark suite, which confirmed MEDBO's significant improvements. Additionally, we applied MEDBO to three engineering problems: pressure vessel design, three-bar truss design, and spring design. MEDBO exhibited an excellent performance in these applications, demonstrating its practicality and efficacy in real-world problem-solving contexts.

Gene selection and cancer classification using interaction-based feature clustering and improved-binary Bat algorithm

In high-dimensional gene expression data, selecting an optimal subset of genes is crucial for achieving high classification accuracy and reliable diagnosis of diseases. This paper proposes a two-stage hybrid model for gene selection based on clustering and a swarm intelligence algorithm to identify the most informative genes with high accuracy. First, a clustering-based multivariate filter approach is performed to explore the interactions between the features and eliminate any redundant or irrelevant ones. Then, by controlling for the problem of premature convergence in the binary Bat algorithm, the optimal gene subset is determined using different classifiers with the Monte Carlo cross-validation data partitioning model. The effectiveness of our proposed framework is evaluated using eight gene expression datasets, by comparison with other recently published algorithms in the literature. Experiments confirm that in seven out of eight datasets, the proposed method can achieve superior results in terms of classification accuracy and gene subset size. In particular, it achieves a classification accuracy of 100% in Lymphoma and Ovarian datasets and above 97.4% in the rest with a minimum number of genes. The results demonstrate that our proposed algorithm has the potential to solve the feature selection problem in different applications with high-dimensional datasets.

Development and simulation of two novel indoor odor source localization methods using a modified shark smell optimization algorithm

Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell Optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.

Quantum Dynamical Interpretation of the Mean Strategy.

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution.