Swarm Intelligence Research Articles

Improvement to application of PSO algorithm on TSP problems

In the field of swarm intelligence algorithms, particle swarm optimization algorithms have the ability of fast search and are widely used in lots of application scenarios because they are easy to implement. Therefore, different improvements have been made for their various properties. In this paper, aiming to accelerate its convergence speed, we further improve the existing hybrid particle swarm algorithm by using the idea of self adaptive and simulated annealing, examining its performance by solving Travelling salesman problem. It demonstrate that the relevant improvements can optimize the algorithm performance and have better convergence speed when iteration value was set 30, 50, or 70 by making comparions between the experiment results of both algorithm. The code used in this article is available on github. The experimental results show that the optimization proposed in this paper can optimize the algorithm to a certain extent at 30, 50, and 70 iterations, mainly because the improved algorithm can keep the convergence speed stable in more iterations.

Research on the hybrid chaos-coud salp swarm algorithm

Salp swarm algorithm (SSA) is a new swarm intelligence optimization algorithm, which has the advantages of simple structure, almost no parameter setting. However, SSA also has the shortcomings of slow convergence speed in the early stage and low optimization accuracy in the later stage when searching for the optimal solution. To address the problems, this study proposes a hybrid chaos-cloud salp swarm algorithm (CC-SSA). First, to accelerate the convergence speed, a positive normal cloud generator is used to perform local search on the superior salp individuals. Second, to enhance the diversity of CC-SSA and avoid it from falling into local optimum, the chaotic map is used to perform global search on the inferior salp individuals by adding global perturbation. Third, to control the execution ratio of global and local search, the mixed control parameter (MLMS) and population allocation coefficient (r) are introduced to organically combine three algorithms, SSA, chaotic map, and cloud model. Finally, to evaluate the performance of the proposed algorithm, it is compared with other 9 conventional algorithms on 12 classic functions. The experimental results show that CC-SSA has an average accuracy rate of 92.92 %, which ranks first. In addition, the average iteration of CC-SSA scores the third. Therefore, compared to conventional optimization algorithms, CC-SSA has better performance in terms of execution time and optimize accuracy.

Optimization of the Colony Size Parameter in the Ant Algorithm for Solving the Routing Problem in Communication Networks

With the increasing amount of information and complexity of communication networks, the issue of routing remains relevant. The routing problem is a classic problem of building a logical structure of a communication network. To solve this problem, many methods are used, such as: genetic algorithms, ant algorithm, swarm intelligence, artificial bee colony algorithm, firefly algorithm, bacterial food search optimization algorithm, etc. Despite the fact that the effectiveness of using the ant algorithm is widely known and described, however, there is no single approach to finding the optimal use case for the algorithm. There is a need to determine the optimal size of the colony when solving the routing problem. The article describes the results of the simulation process of modeling the behavior of ant colonies of different sizes to find the optimal route through the AnyLogic environment. It is determined that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks. To find the optimal ratio of the colony size to the complexity of the task, you can use the grid search methodology. The results clearly demonstrate that with increasing complexity of the task (the number of communication nodes), the same number of ants cope worse with the task. The problem associated with the need to accurately determine the optimal size of a colony is of great practical importance, because an increase in the size of a colony has generally decreasing utility. It was found that for many, an increase in the number of ants from 100 to 500 had a relatively greater effect than an increase in the size of the colony from 1000 to 10000. It is proved that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks.

Simulation based swarm intelligence optimization to develop manufacturing distribution plan

Simulation based swarm intelligence optimization to develop manufacturing distribution plan

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Comparism of Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in Optimisation of Soybean Soapstock Biodiesel Production

Soybean soapstock (SS), a lipid rich by-product of soybean oil production is a promising feedstock for the production ofbiodiesel due to its availability and affordability. In the esterification and transesterification reactions involving soyabeansoapstock, sodium hydroxide, methanol and n-hexane were used as catalyst, solvent and co-solvent respectively. The physico-chemical properties of the biodiesel obtained were determinedusing the Association of Analytical Chemist (AOAC) and American Society of Testing Materials (ASTM) methods. The esterification and transesterification reactions were optimised using both response surface methodology (RSM) under design expert 7.0 platform and Particle swarm technique in ANFIS (ANFIS-PSO) using the MATLAB software. The optimized acid value from the esterification reaction using RSM and ANFIS-PSO were 4.956 and 1.488 while the yield obtained were 97.29% and 99.91%respectively with ANFIS-PSO proving to be the better optimization technique in both cases. Comparison plots made for both reactions shows the ANFIS-PSO curve mirroring the experimental and thus signifying a closer trend when compared to the RSM curve. The suitability of the ANFIS-PSO prediction was further highlighted by the error analysis carried out on both techniques. The Residual sum of squares (RSS), Mean absolute error (MAE), Root mean square error (RMSE), Correlation coefficient (R), Coefficient of determination (R<sup>2</sup>), Adjusted R<sup>2</sup>, Absolute average deviation (AAD) and Mean absolute percent error (MAPE) values for the ANFIS-PSO predictions in both reactions were better than the RSM predictions. It can thus be concluded that soybean soapstock is a viable feedstock for biodiesel production and ANFIS-PSO is a more efficient optimization technique when compared with RSM in esterification and transesterification of soybean soapstock.

PARALLEL PLATFORM CONTROLLER BASED ON ADAPTIVE DIFFERENCE ALGORITHM – PART 1

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 details the research background and the algorithm design process. Then, the MPC algorithm is implemented on the upper computer using C++, and the physical test is implemented. The test results show that the controller has a good control effect on the physical platform.

A novel contact optimization algorithm for endomicroscopic surface scanning.

Probe-based confocal laser endomicroscopy (pCLE) offers real-time, cell-level imaging and holds promise for early cancer diagnosis. However, a large area surface scanning for image acquisition is needed to overcome the limitation of field-of-view. Obtaining high-quality images during scanning requires maintaining a stable contact distance between the tissue and probe. This work presents a novel contact optimization algorithm to acquire high-quality pCLE images. The contact optimization algorithm, based on swarm intelligence of whale optimization algorithm, is designed to optimize the probe position, according to the quality of the image acquired by probe. An accurate image quality assessment of total co-occurrence entropy is introduced to evaluate the pCLE image quality. The algorithm aims to maintain a consistent probe-tissue contact, resulting in high-quality images acquisition. Scanning experiments on sponge, ex vivo swine skin tissue and stomach tissue demonstrate the effectiveness of the contact optimization algorithm. Scanning results of the sponge with three different trajectories (spiral trajectory, circle trajectory, and raster trajectory) reveal high-quality mosaics with clear details in every part of the image and no blurred sections. The contact optimization algorithm successfully identifies the optimal distance between probe and tissue, improving the quality of pCLE images. Experimental results confirm the high potential of this method in endomicroscopic surface scanning.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Method for complex increase of welding production control efficiency based on swarm intelligence algorithms and evolutionary modeling

Method for complex increase of welding production control efficiency based on swarm intelligence algorithms and evolutionary modeling

Genetic Algorithms and Swarm Intelligence in Computational Mathematics for Predictive Modelling

The integration of genetic algorithms and swarm intelligence into computational mathematics has significantly advanced the field of predictive modeling. Genetic algorithms use the ideas behind natural selection and genetics to find the best answers to difficult problems over and over again. These algorithms are great at working with big, multidimensional search areas. This makes them perfect for many predictive modeling tasks, such as genomics, financial predictions, and climate modeling. The idea behind swarm intelligence comes from the way social animals like ants, bees, and birds act as a group. It uses autonomous, self-organized systems to improve predictive models. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two examples of techniques that use simple agents that follow basic rules to solve difficult optimization problems. In order to improve and confirm models, these programs often use computational mathematics methods like linear algebra, statistical analysis, and differential equations. When you combine genetic algorithms, swarm intelligence, and these mathematical methods, you get a strong framework for dealing with the computational problems that come up in predictive modeling. This combined method makes it easier to explore and use the search area effectively, raises the rate of convergence, and improves the quality of the solutions. This combination can be used in many areas, such as engineering to improve design parameters, economics to guess market trends, and healthcare to make diagnoses more accurate. In addition, the fact that these methods can be changed to include domain-specific knowledge adds to their usefulness and flexibility. Genetic algorithms and swarm intelligence are continuing to improve with the help of more complex mathematical and computational methods. This will lead to even better tools for making predictions, which will encourage new ideas in many scientific and economic fields.

Fresh produce supply chain network design and management using swarm intelligence: A case study of Egypt

Fresh produce supply chain network design and management using swarm intelligence: A case study of Egypt

Application of artificial intelligence combined with near infrared spectroscopy in the continuous counter-current extraction process of Angelica dahurica formula granules

The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R2v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R2v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI.

Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm’s performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

Artificial Bee Colony Algorithm to Optimize the Safety Distance of Workers in Construction Projects

This paper presents the results of a simulation model regarding the productivity and safety working space for construction workers through the floors of a building using swarm intelligence (SI), a field of artificial intelligence (AI), and specifically using artificial bee colony (ABC) optimization. After designing the algorithm used to build the simulation model, the simulation was used in an actual building project by comparing the travel times of workers conventionally transporting material with another group working on routes optimized by the algorithm. Thus, the proposed algorithm provides routes combining shorter travel times and correct distances between workers when transporting materials in a construction site, handling the interference between crews. After validating the algorithm on-site, no statistically significant differences were found between the travel times of workers and the times delivered by the algorithm. Additionally, the travel times using the routes obtained through the algorithm were significantly lower than those made by workers who moved freely without a predefined route. In summary, the algorithm proposed may help construction practitioners maintain safe movements that respond to hazard contexts imposed by any restriction that demands a safety distance.

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

Seismic Response Prediction of Porcelain Transformer Bushing Using Hybrid Metaheuristic and Machine Learning Techniques: A Comparative Study

Although seismic response predictions are widely used for engineering structures, their applications in electrical equipment are rare. Overstressing at the bottom of the porcelain insulators during seismic events has made power transformer bushings in substations prone to failure. Thus, this paper proposed and compared six integrated machine learning (ML) models for seismic stress response predictions for porcelain transformer bushings using easily monitored acceleration responses. Metaheuristic algorithms such as particle swarm optimization were employed for architecture tuning. Prediction accuracies for stress response values and classifications were evaluated. Finally, shaking table tests and simulation analyses for a 1100 kV bushing were implemented to validate the accuracy of the six ML models. The results indicated that the proposed ML models can quickly forecast the maximum stress experienced by a porcelain bushing during earthquakes. Swarm intelligence evolutionary technologies could quickly and automatically aid in the retrofitting of architecture for the ML models. The K-nearest neighbor regression model had the best level of prediction accuracy among the six selected ML models for experimental and simulation validations. ML prediction models have clear benefits over frequently used seismic analytical techniques in terms of speed and accuracy for post-earthquake emergency relief in substations.

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic. Keywords: optimization, swarm intelligence, mathematical modelling, nonlinear regression, complex objects and systems.

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation

The Graph Coloring Problem (GCP) is a significant optimization challenge widely suitable to solve scheduling problems. Its goal is to specify the minimum colors (k) required to color a graph properly. Due to its NP-completeness, exact algorithms become impractical for graphs exceeding 100 vertices. As a result, approximation algorithms have gained prominence for tackling large-scale instances. In this context, the Cat Swarm algorithm, a novel population-based metaheuristic in the domain of swarm intelligence, has demonstrated promising convergence properties compared to other population-based algorithms. This research focuses on designing and implementing the Cat Swarm algorithm to address the GCP. By conducting a comparative study with established algorithms, our investigation revolves around quantifying the minimum value of k required by the Cat Swarm algorithm for each graph instance. The evaluation metrics include the algorithm's running time in seconds, success rate, and the mean count of iterations or assessments required to reach a goal.