Artificial Fish Swarm Algorithm Research Articles

Faults locating of power distribution systems based on successive PSO-GA algorithm

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information.

Optimization of parallel SVM algorithm for big data

Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation

Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets.

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.

Adaptive positioning method for ball head based on impedance control

PurposeComponent positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.Design/methodology/approachFirst, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.FindingsDuring the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.Originality/valueIn this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection

PurposeThis study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.Design/methodology/approachThis study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.FindingsThe optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.Originality/valueThis study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

Thermal resistance optimization of ultra-thin vapor chamber based on data-driven model and metaheuristic algorithm

The ultra-thin vapor chamber(UTVC) is extensively utilized across various fields due to its excellent heat dissipation performance and good temperature uniformity. The data-driven modeling approach is well suited to predict the thermal resistance of the UTVC due to its great flexibility and accuracy. In this paper, a novel approach is proposed to optimize the UTVC thermal resistance, which combines the radial basis function neural network(RBFNN) model with an improved adaptive differential fish swarm evolution algorithm(ADFEA). The mean square error of the RBFNN model was 0.00016 on the training set and 0.00027 on the test sets, which indicates that the model is able to accurately predict the thermal resistance of the UTVC. The data are obtained from experiments on a mesh wick UTVC with dimensions of 124 × 14 × 1 mm. A novel optimization algorithm, ADFEA, has been designed to enhance optimization capability and convergence accuracy. This algorithm combines differential algorithm and artificial fish swarm algorithm, incorporating a parameter adaptation mechanism. The optimal operating parameters of the UTVC are obtained by ADFEA optimization and the accuracy of the optimized results is verified by experiment. The proposed optimization method provides new insights for the design and optimization of UTVC.

Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using Machine Learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self-Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO's superiority. The study identifies the significant impact of ICT on self-progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision-making. Further exploration in real-world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

Research on automatic driving strategy optimization method of high-speed train based on artificial fish swarm algorithm

The existing automatic driving technology of high-speed railway trains has been able to complete the automatic driving of trains in normal scenes, and it has begun to develop in the direction of improving operational efficiency and optimizing driving strategies. Based on the current automatic driving technology, this paper proposes an optimization method of automatic driving strategy for high-speed trains based on an artificial fish swarm algorithm, aiming at reducing train energy consumption, improving train parking accuracy, train punctuality, passenger comfort, and train speed protection. Firstly, based on train dynamics, the dynamic parameter model of the train in the running process is described. Through the train dynamic parameter model, combined with line information such as speed limit, slope, and phase separation area, an artificial fish swarm algorithm is designed that aims at safety, punctuality, energy saving, comfort, and parking accuracy. The artificial fish swarm algorithm is used to simulate the optimal driving strategy of train running and generate the train driving curve. Finally, the driving curve simulated by the artificial fish swarm algorithm is compared with the driving curve simulated by the genetic algorithm and the actual driving curve of the Beijing-Shenyang high-speed train, which shows that the proposed method is effective and the convergence speed of the artificial fish swarm algorithm is better than that of the genetic algorithm.

Individual Adaptive Regulation Strategy Inspired by Artificial Fish Swarm Algorithm for Tumor Targeting

Individual Adaptive Regulation Strategy Inspired by Artificial Fish Swarm Algorithm for Tumor Targeting

Discrete Artificial Fish Swarm Algorithm-Based One-Off Optimization Method for Multiple Co-Existing Application Layer Multicast Routing Trees

As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes.

A bidirectional collaborative method based on an improved artificial fish swarm algorithm for ship pipe and equipment layout design

Ship engine room layout design (SERLD) significantly impacts a ship's transportation efficiency and safety by focusing on the layouts of equipment and piping. However, owing to complex constraints, previous research has mainly focused on single-dimensional layout designs and has failed to provide comprehensive references for designers. To address this research gap, this study proposes a collaborative layout method based on a multistrategy hybrid-objective artificial fish swarm algorithm (HMSAFSA). In terms of the underlying mathematical representation, a more stable Manhattan trajectory-based coding method suitable for a collaborative layout is proposed. Building on this coding method, multiple strategies are incorporated into the heuristic AFSA to enhance its optimization and collaborative performance. Collaborative evaluation functions and methods are designed and refined to ensure effective layout results for multiple objectives. Furthermore, a layout procedure incorporating bidirectional guidance strategies and hierarchical thinking is proposed. This method achieves collaborative layouts through the mutual guidance of optimal objectives. Finally, the effectiveness of the proposed method is verified through representative cases of various types of ship engine rooms in practical engineering. The method demonstrates its capability to offer multiple optimal layout schemes, thus presenting substantial value for practical engineering designs.

A physics-informed artificial fish swarm algorithm for multiple tunnel fire source locations prediction

To address multiple tunnel fire source locations prediction for better firefighting, this work aims to establish a model-driven and data-driven fusion algorithm, which is named as the physics-informed artificial fish swarm algorithm. In the algorithm, the artificial fish swarm algorithm is utilized to avoid complex fire mechanisms modelling, which makes the algorithm owns the advantage of the data-driven methods. Meanwhile, a physical model of the longitudinal ceiling temperature distribution relative to fire source locations and some physical mechanisms are utilized to guide the data-driven artificial fish swarm algorithm for converging to the reasonable prediction, which makes the algorithm owns the advantage of the model-driven methods. Two full-scale tunnel fire experiments with two pool fire sources and three pool fire sources as well as one full-scale cable fire experiment in the tunnel are utilized to support and verify the algorithm, which can be utilized to achieve multiple tunnel fire sources prediction and ceiling temperature distribution prediction in tunnel fires.

New Computing Tasks Offloading Method for MEC Based on Prospect Theory Framework

Mobile edge computing (MEC) provides reliable solutions for networked vehicles, mobile phones, and other mobile devices to complete intensive computing and delay-sensitive tasks. Most of the existing studies design a series of methods to achieve the expected goals based on assuming that users are absolutely rational. However, due to the subjectivity of users, the actual results will deviate from the real situation. Using the prospect theory (PT) framework, this article studies the problem of computing task offloading in real situations. Aiming at the task offloading problem in the small cellular network scenario, the artificial fish swarm algorithm is used to optimize the system energy with limited delay. Finally, the experimental tests verify the impact of user behavior on the system energy optimization and the effectiveness of the task offloading method proposed in this article.

Human-Induced Vibration Control of Floor Structures Using MTMD System Optimized by MATLAB-SAP2000 Interface

Under human-induced excitations, a floor structure may suffer excessive vibrations due to its large span and low damping ratio. Vertical vibrations, in particular, can become intolerable during resonance events. A tuned mass damper (TMD) is a widely used single-degree-of-freedom dynamic vibration absorber. To enhance the serviceability of a floor structure, a multiple TMD (MTMD) system finds broad application. The parameters of the MTMD must be carefully designed to achieve satisfactory performance. However, existing studies often employ a simplified model of the floor structure with closely spaced modes to optimize the parameters of MTMD. Nonetheless, an oversimplified floor model can lead to a reduction in its control effect. To solve this problem, this study utilizes the OAPI facility of SAP2000 to build a connection with MATLAB. A multi-objective optimization algorithm based on the artificial fish swarm algorithm (AFSA) for MTMD is developed in MATLAB, while the finite element model of a real floor structure is built in SAP2000. The locations of the MTMD system are initially specified in SAP2000 and, through the proposed MATLAB–SAP2000 interface, data can be exchanged between them. Based on the structural dynamic responses to external excitations in SAP2000, the optimization process for the MTMD is carried out in MATLAB. Concurrently, the parameters of the MTMD in SAP2000 are iteratively adjusted until they reach their final optimal values. To underscore the enhancements brought about by the proposed interface and optimization method, a comparative case study is conducted. A group of MTMDs, optimized using a conventional method, is presented for reference. The numerical results indicate that, overall, the proposed MTMD system exhibits superior control effectiveness and robustness.

A hybrid approach to water potability prediction: leveraging artificial fish swarm algorithm and convolutional neural networks

A hybrid approach to water potability prediction: leveraging artificial fish swarm algorithm and convolutional neural networks

Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms.

Adversarial Attack Model Based on Deep Neural Network Interpretability and Artificial Fish Swarm Algorithm

Adversarial Attack Model Based on Deep Neural Network Interpretability and Artificial Fish Swarm Algorithm