Swarm Intelligence Research Articles

Application of Immunological and Swarm Intelligence Learning-Based Algorithm for Industrial Grade Computer Sales Prediction

ABSTRACT This paper strives to raise the imitating effectiveness of radial basis function-based neural network (RNNet) through biological learning (BL) and swarm intelligence (SI) optimization algorithms. Latter, the artificial immune system (AIS) and particle swarm optimization (PSO) algorithms are utilized for RNNet to regulate. The proposed synthesis of AIS-inspired and PSO-inspired (SAIPS) algorithm incorporates the complementary development and prospecting abilities to realize optimized resolution. The attribute of population variation has shown high frequency to meet the global optimum to replace local optimum being restricted and outperforms in five standard nonlinear trial functions. The experimental results have represented that the consolidation of AIS-inspired and PSO-inspired algorithms is an outstanding approach and therefore a hybrid algorithm is proposed, which aims to obtain an expression that can cultivate optimum precision among related algorithms in this research. The algorithm then evaluates results from five standard inspections and an empirical industrial grade computer (IgC) sales prediction instance in Taiwan, which reveals that the proposed SAIPS algorithm exceeds the performance among related algorithms as well as the relevant auto-regressive integrated moving average (ARIMA) models in terms of accuracy and time spent.

Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency.

CSTR parameter identification and PID control optimization based on improved swarm intelligence algorithm

Abstract Aiming at the problems of strong coupling and large hysteresis that exist in the production process of a typical thermal object continuous stirred reactor (CSTR), which make it difficult to recognize the system model parameters and control the temperature, an improved firefly algorithm is proposed to recognize the system parameters, and its recognition accuracy is higher than that of the original firefly algorithm; and an improved sparrow search algorithm (BCGSSA) is used to control the temperature of the CSTR system by combining the BCGSSA algorithm with PID control, which significantly improves the dynamic performance of the CSTR system. Then an improved sparrow search algorithm (BCGSSA) is combined with PID control to control the temperature of the CSTR system, which significantly improves the dynamic performance of the CSTR, and the improved algorithm (BCGSSA) has good control accuracy and robustness.

Particle swarm optimization with local search for height-map surface reconstruction from point clouds in reverse engineering

AbstractSurface reconstruction is a classical process in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned or digitized and the resulting point cloud is then fitted to mathematical surfaces through numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Real-world objects such as manufactured workpieces often require complex nonlinear approximating functions, which are not well suited for standard numerical methods. In a previous paper presented at the ISMSI 2023 conference, we addressed this issue by using manually selected approximation functions via optimization through the cuckoo search algorithm with Lévy flights. Building upon that work, this paper presents an enhanced and extended method for surface reconstruction by using height-map surfaces obtained through a combination of exponential, polynomial and logarithmic functions. A feasible approach for this goal is to consider continuous bivariate distribution functions, which ensures consistent models along with good mathematical properties for the output shapes, such as smoothness and integrability. However, this approach leads to a difficult multivariate, constrained, multimodal continuous nonlinear optimization problem. To tackle this issue, we apply particle swarm optimization, a popular swarm intelligence technique for continuous optimization. The method is hybridized with a local search procedure for further improvement of the solutions and applied to a benchmark of 15 illustrative examples of point clouds fitted to different surface models. The performance of the method is analyzed through several computational experiments. The numerical and graphical results show that the method is able to recover the shape of the point clouds accurately and automatically. Furthermore, our approach outperforms other alternative methods significantly in terms of the numerical errors. We conclude that our method can be successfully applied to the reconstruction of manufactured workpieces in real industrial settings.

A Review of Smart Camera Sensor Placement in Construction

Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes.

Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms

Nonlinear, complex optimization problems are prevalent in many scientific and engineering fields. Traditional algorithms often struggle with these problems due to their high dimensionality and intricate nature, making them time-consuming. Many researchers have proposed new metaheuristic algorithms inspired by biological behaviors in nature, which comparatively show higher performance and accuracy than traditional optimization algorithms. Nature-inspired algorithms, particularly those based on swarm intelligence, offer adaptable and efficient solutions to these challenges. In recent years, swarm intelligence algorithms have made significant advancements. Classical and CEC benchmark suits are immersively useful for studying the performance of optimization algorithms. According to our literature survey, we identified that many algorithms were evaluated based on accuracy. Currently, swarm intelligence algorithms are used in many applications, and efficiency and computational complexity need to be evaluated. A broad-level study of the computational complexity and accuracy of popular swarm intelligence algorithms has not been done recently. Therefore this study we comprehensively evaluate and compare 21 bio-inspired swarm intelligence algorithms on eight non-separable unimodal, eight separable unimodal, five non-separable multimodal, seven separable multimodal functions, and two CEC 2018 many objective functions. We study the structure and mathematical model of the selected algorithms. Then we categorized selected algorithms into six different behavioral groups. We calculated the root mean square error between expected and actual values. Then we performed an RMSE cross-validation statistical test to understand how accurately an algorithm resolves an average problem. We found that Artificial Lizard Search Optimization (ALSO) is the most prominent algorithm in accuracy and efficiency. Besides that, Cat Swarm Optimization (CSO), Squirrel Search Algorithm (SSA), and Chimp Optimization Algorithm (CHOA-B) are also considered more universal algorithms. The Squirrel Search Algorithm (SSA) is ALSO’s second-best algorithm in time complexity. Wasp Swarm Algorithm (WSO), and Bat-Inspired Algorithm (BA) presented the lowest time complexity. Finally, several important issues and research directions are discussed.

Optimum arrangement of high-voltage transmission line conductors by utopian Pareto swarm intelligence

Optimum arrangement of high-voltage transmission line conductors by utopian Pareto swarm intelligence

Magnetic swarm intelligence of mass-produced, programmable microrobot assemblies for versatile task execution

Magnetic swarm intelligence of mass-produced, programmable microrobot assemblies for versatile task execution

Study on bionics-based swarm intelligence optimization algorithms for wavelength selection in near-infrared spectroscopy

Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.

AI and Multi-Agent Systems: Collaboration and Competition in Autonomous Environments

Cutting edge technology in intelligence involves multi agent systems (MAS) which allow autonomous agents to interact in shared environments by either working together or competing to achieve common or individual goals. This study delves into the aspects of cooperation and rivalry in MAS and illustrates their application in practical situations, like autonomous vehicles, robot’s interactions, and financial settings. In addition to that we explore the obstacles like coordination, learning and communication that come up while creating MAS frameworks and how sophisticated algorithms like deep reinforcement learning help in running these agents. By tackling both competitive interactions within MAS our goal is to offer a thorough grasp of the possible uses and upcoming paths, in this area. Emerging technologies like OpenAIs agent models play a significant role in showcasing the changing landscape of MAS and its transformative effects on various industries, like healthcare and defense. Keywords: Multi-Agent Systems (MAS), Autonomous Agents, Collaboration, Competition, Deep Reinforcement Learning, Game Theory, Distributed AI, Swarm Intelligence, Agent-Based Modeling, AI Coordination, Adversarial AI

Advancements in Machine Learning for Pipeline Integrity Management: A Comprehensive Review of Predictive and Optimization Techniques

Oil and gas pipeline networks are crucial component of energy infrastructure. However, as the integral elements of the energy transportation system are evolving towards Industry 4.0 and Smart Manufacturing, pipelines network are progressively shifting towards intelligence and digitization. The fields of asset integrity management, operation inspection, corrosion prevention, leak and intrusion detection, and flow assurance are just a few of the areas where machine learning and artificial intelligence (AI) algorithms are becoming more and more important. The predictive and optimization capabilities of these Models/ Algorithms have been leveraged by various Pipeline Network Integrity Management systems to avert pipeline failure, reduce environmental damage, and effectively allocate resources. This study reviews and gives a summary of current advancements in intelligent techniques, encompassing heuristic computations, mathematical programming, and machine learning techniques used in a pipeline network integrity management to enhance various operational and maintenance aspects. This work makes two intellectual accomplishments. Firstly, it offers a technical review of literatures, systematically synthesizing different computational intelligence and machine learning approaches previously applied, along with their methodologies, contributions, drawbacks, and comparisons. Secondly, the study recommends some nature-inspired meta-heuristic algorithms, which encompasses Evolutionary and the Swarm Intelligence Algorithms to be applied for future directions and challenges. Finally, the study underscores the potential of computational intelligence and algorithms for learning methods in predicting and optimizing the efficiency of pipeline network operation and management, emphasizing the necessity for further development of models/algorithms and application to enhance more intricate interactions between pipeline infrastructure monitoring, maintenance, and management.

Employee Turnover Prediction based on Particle Swarm Optimization - Support Vector Machine

Employee turnover is getting more and more attention in the human resources field. Unexpected turnover of employees is blamed for the loss of work handover. As a result, predicting whether employees would leave has become a crucial problem. This research aims to exploit a method combining particle swarm optimization with a support vector machine to address the employee departure prediction problem. In this study, the particle swarm optimization algorithm is used to optimize the parameter selection of the support vector machine to improve the performance of the latter. Moreover, employee information of a dataset is subject to correlation analysis before being transformed into standardization form to accelerate convergence and improve the accuracy of the support vector machine. Eventually, the support vector machine combined with particle swarm optimization is of best performance in accuracy score, precision score and F1 score, respectively reaching 0.873, 0.947 and 0.784. In conclusion, this method addresses the employee turnover prediction problem effectively which also provides a new direction for applying swarm intelligence algorithms.

Random Exploration and Attraction of the Best in Swarm Intelligence Algorithms

Random Exploration and Attraction of the Best in Swarm Intelligence Algorithms

An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks

This manuscript presents ABSAS-CS-GSA, an improved iteration of the beetle antennae search (BAS) algorithm, tailored to address the shortcoming of the BAS with low convergence accuracy and easily falling into local optima. The improvements are structured into three principal components: first, dynamic step-size adjustment predicated on initial population to bolster solution accuracy and expedite convergence; second, position update mechanism integrating golden sine algorithm to diversify search patterns and expedite convergence; and third, population disturbance based on vertical and horizontal cross strategy to avoid falling into local optima. Experimental results on 12 benchmark functions demonstrate ABSAS-CS-GSA's superiority over the standard BAS and its four variants, as well as over two classic swarm intelligence algorithms. The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. The refined algorithm exhibits excellent performance and is well-suited for tackling the coverage issue within wireless sensor networks.

Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

ABSTRACTSwarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.

EXPRESS: Tourism demand interval forecasting with an intelligence optimization-based integration method

Interval forecasting for tourism demand holds significant theoretical and practical insights. However, research on integrating social reviews into multi-source for interval prediction is still developing. To fill this research gap, this study proposes an integrated method for tourism demand interval prediction by combining multi-source data with a modified swarm intelligence optimizer. This method can extract essential intrinsic features from multi-source and select an appropriate probability density function to extend point predictions to initial prediction intervals, then further refine the initial prediction intervals to improve the prediction accuracy. Empirical studies on the tourism demand of Mount Siguniang and Jiuzhaigou validate the superior predictive capabilities of the proposed model. Experimental results demonstrate that (a) incorporating a multi-source dataset with social reviews significantly enhances the accuracy of the proposed model; (b) the modified transit search algorithm effectively balances the coverage and width of prediction intervals, thus improving the generalizability of the model.

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

A Swarm Intelligence Technique for Link Monitoring Problems in Wireless Ad Hoc Networks

Multiple strategies improved spider wasp optimization for engineering optimization problem solving

The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.

SLDPSO-TA: Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization

In modern circuit design, the short-circuit problem is one of the key factors affecting routability. With the continuous reduction in feature sizes, the short-circuit problem grows significantly in detailed routing. Track assignment, as a crucial intermediary phase between global routing and detailed routing, plays a vital role in preprocessing the short-circuit problem. However, existing track assignment algorithms face the challenge of easily falling into local optimality. As a typical swarm intelligence technique, particle swarm optimization (PSO) is a powerful tool with excellent optimization ability to solve large-scale problems. To address the above issue, we propose an effective track assignment algorithm based on social learning discrete particle swarm optimization (SLDPSO-TA). First, an effective wire model that considers the local nets is proposed. By considering the pin distribution of local nets, this model extracts and allocates more segments to fully leverage the role of track assignment. Second, an integer encoding strategy is employed to ensure that particles within the encoding space range correspond one-to-one with the assignment scheme, effectively expanding the search space. Third, a social learning mode based on the example pool is introduced to PSO, which is composed of other particles that are superior to the current particle. By learning from various objects in the example pool, the diversity of the population is improved. Fourth, a negotiation-based refining strategy is utilized to further reduce overlap. This strategy intelligently transfers and redistributes wire segments in congested areas to reduce congestion across the entire routing panel. Experimental results on multiple benchmarks demonstrate that the proposed SLDPSO-TA can achieve the best overlap cost optimization among all the existing methods, effectively reducing congestion in critical routing areas.

Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization

Optimization of real-world problems is an important challenge that researchers face due to the discrete nature of the search space, multiple local optimum, the large dimension of the problem, and computation costs. The Special Relativity Search (SRS) algorithm is one of the newest metaheuristic methods that has recently been developed. In this work, the Enhanced SRS (ESRS) algorithm has been developed based on the Lorentz Force coefficient to solve this class of problems. The SRS is a single-objective algorithm based on swarm intelligence inspired by the physics of special relativity. The cause of movement between particles in magnetic space is the Lorentz Force. Due to the type of particle charge, this force repels or attracts particles. SRS performance is sensitive to the Lorentz Force. Developing an empirical equation can enhance the performance of the algorithm. An empirical equation for simulating the Lorentz Force Coefficient is proposed as LC. LC decreases proportionally to the number of different iterations. This equation has a constant coefficient to control the movement step. This constant is more accurate for small values than for large values. Several structural problems with discrete and continuous variables have been investigated to evaluate the performance of the proposed algorithm. The objective function is the weight of structural elements that are under loading and must be reduced to the minimum value while observing the constraints of the problem. ESRS results have been compared with the original SRS and several state-of-the-art algorithms. The results show that ESRS has obtained the best optimal weight with the least computational effort and with high accuracy.