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Solving urban electric transit network problem by integrating Pareto artificial fish swarm algorithm and genetic algorithm

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2022年第3期26卷 253-268页

作者： Liu, Yi Feng, Xuesong Yang, Yan Ruan, Zejing Zhang, Lukai Li, Kemeng Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Guangxi Traff Technician Coll Nanning Peoples R China

This study presents a multi-objective optimization model for the urban electric transit network problem with the aim of simultaneously designing the layout of bus routes, the frequency and the location and size of charging stations by making a tradeoff between two inconsistent objectives from the perspectives of passengers and operators. A Pareto artificial fish swarm algorithm (PAFSA) embedded with the genetic algorithm (GA) is developed to solve the proposed model. The PAFSA is designed to iteratively search for the proper network configuration satisfying two conflicting objectives. During which, the demand assignment with real-time transit information is performed to update the frequency of each newly designed route. The GA embedded into the PAFSA iteratively decides the locations of charging stations and the number of chargers to be installed in each charging station. A case study of the transit network in an urban region of a city in China is implemented, revealing that the proposed approach is able to rationally design a relatively large-scaled transit network with searching for the best fits between two inconsistent objectives.

关键词： artificial fish swarm algorithm charging station location frequency setting multi-objective optimization transit network design

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Band-Area Application Container and artificial fish swarm algorithm for Multi-Objective Optimization in Internet-of-Things Cloud

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IEEE ACCESS 2022年 10卷 16408-16423页

作者： Mingxue, Ouyang Xi, Jianqing Bai, Weihua Li, Keqin South China Univ Technol Sch Software Engn Guangzhou 5106 Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing 526061 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Container virtualization methods based on application deployment levels have been widely adopted in cloud-computing environments to implement application construction, deployment, and migration. However, most application containers focus on the interface between the applications and hosts and lack collaboration between application containers. This study proposes a new application container model that contains users, application services, documents, and messages, called Band-area Application Container. A salient feature of the Band-area is that it can express a variety of things in reality, such as organizations or individuals. End users can build a complex and changeable application system through cooperation between the Band-areas. However, the resource allocation of non Internet-of-Thing and Internet-of-Thing tasks from the application container is an open issue. The resource allocation method of tasks should not only improve the quality of the user experience, but also reduce energy consumption by improving the resource utilization of the server. To solve this problem, an artificial fish swarm algorithm is proposed to optimize container-based task scheduling. The algorithm considers not only the reliability, processing time overhead, and energy consumption of the task, but also the resource utilization of the servers. Experimental evaluation shows that, compared with the existing three algorithms, the algorithm obtains a better improvement rate in task processing time overhead, energy consumption, reliability, and cluster load balancing.

关键词： Containers Task analysis Resource management Optimization Computational modeling Scheduling Cloud computing Application container artificial fish swarm algorithm Internet-of-Things multi-objective optimization task scheduling

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Research and Implementation of Parallel artificial fish swarm algorithm Based on Ternary Optical Computer

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MOBILE NETWORKS & APPLICATIONS 2022年第4期27卷 1397-1407页

作者： Li, Shuang Li, Wenjing Wang, Zhehe An, Dongdong Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 201418 Peoples R China Nanning Normal Univ Sch Logist Management & Engn Nanning 530299 Guangxi Peoples R China Hainan Trop Ocean Univ Coll Comp Sci & Technol Sanya 572022 Peoples R China

artificial fish swarm algorithm (AFS) is used in the field of function optimization problems widely. The traditional AFS algorithm has some problems such as long time to find the optimal solution and easy to fall into local optimality at the later stage of the search. We investigate design solutions and methods to implement parallel AFS algorithms by taking advantage of the large number of TOC processor bits and the easy scalability of processor bits. Firstly, we find out the parallel part of the algorithm by analyzing the traditional AFS algorithm and carry out the parallel design. Then we performed a detailed design of the algorithm implementation flow and analyzed the clock cycle. Finally, the correctness of our proposed parallel algorithm is verified on SD11. Compared with the serial AFS and parallel AFS algorithms based on electronic computers, the TOC-based AFS algorithm (TOC-PAFS) proposed in this paper effectively reduces the search time and improves the search performance of complex multi-peaked function optimization problems.

关键词： Ternary optical computer Ternary optical processor artificial fish swarm algorithm Parallel computing

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An early warning model for the stuck-in medical drilling process based on the artificial fish swarm algorithm and SVM

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 779-796页

作者： Xian, Zhongyan Yang, Hai Southwest Petr Univ Sch Mech Engn Chengdu 610500 Peoples R China

To avoid the considerable challenges and losses caused by stuck drilling to normal drilling operations, this article analyses the mechanism of stuck drilling, then combines the artificial fish swarm algorithm (AFSA) and support vector machine (SVM), and finally proposes an early warning model for the stuck-in medical drilling process based on the AFSA and SVM. The model realizes real-time sticking risk warnings by using the four parameters of riser pressure, torque, speed and hook load collected in real time and promotes real-time drilling parameter monitoring for the real-time dynamic warning of sticking risk. By comparing the AFSA-SVM sticking prediction model with the particle swarm optimization model and the traditional cross-validation optimization model, it is found that the AFSA-SVM stuck prediction model accuracy can reach 97.561% and that the training and testing times are 5.874 s and 0.76 s, respectively. Its accuracy and computational efficiency are higher than those of the particle swarm optimization model and traditional cross-validation optimization model. In comparison with the existing technology, the four-parameter early sticking warning model based on AFSA-SVM presented in this paper shows powerful comprehensive performance and field application value.

关键词： Borehole cleaning Stuck drilling Machine learning artificial fish swarm algorithm Vector machine

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Research on coverage holes repair in wireless sensor networks based on an improved artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2022年第4期15卷 312-330页

作者： Dongliang, Li Jiaozuo Univ Fac Informat Engn Jiaozuo 454000 Peoples R China

In a wireless sensor network (WSN), holes are formed when nodes become invalid. To resolve this problem, holes repairing algorithm based on fish swarm optimisation in wireless sensor network (HRFSO) is proposed in this paper. In the algorithm, network coverage is served as the objective function, and the biological behaviours of artificial fish are used to simulate node movements. The new actions of jumping and survival of the fittest are defined besides foraging, rear-ending and grouping to improve the convergence of optimisation. Self-adaptive vision and step length are used when updating the status of artificial fish. Failed holes are repaired by moving the sensor node with the shortest distance. The simulation results show that the algorithm is suitable for repairing holes with fast speed by moving fewer nodes. It can increase WSN coverage with better repairing results, faster convergence, higher accuracy, efficiency, and robustness. The results also show the lifetime of the network can be prolonged.

关键词： network coverage AFSA artificial fish swarm algorithm WSN wireless sensor network hybrid network robustness

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An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routing

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INTELLIGENT DATA ANALYSIS 2022年第1期26卷 239-256页

作者： Yuan, Mengfei Kan, Xiu Chi, Chihung Cao, Le Shu, Huisheng Fan, Yixuan Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Southeast Univ Sch Math Nanjing Jiangsu Peoples R China Data61 CSIRO Hobart Tas Australia Donghua Univ Sch Sci Shanghai Peoples R China

In this paper, the Capacitated Vehicle Routing Problem (CVRP) of multi-depot express delivery is investigated based on the actual express delivery business in Beijing and driving intention-based road network. An Adaptive Simulated Annealing and artificial fish swarm algorithm (A-SAAFSA) is proposed to solve the CVRP. The basic ideas are use a "certainty" probability to accept the worst solution through the Metropolis criterion in the search process, and a strategy of adjusting the swimming direction to avoid falling into the local optimal solution. Moreover, an adaptive visual strategy, which adjusts the visual range adaptively in real time according to the current solution quality, is used to ensure the efficient searching and accuracy of the algorithm. Experimental results show that the A-SAAFSA algorithm outperforms four well-known algorithms, namely simulated annealing and artificial fish swarm algorithm, artificial fish swarm algorithm, simulated annealing algorithm, and genetic algorithm.

关键词： Express delivery CVRP road network simplification artificial fish swarm algorithm simulated annealing algorithm adaptive visual strategy

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Energy consumption parameter detection of green energy saving building based on artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第5-6期44卷 498-510页

作者： Yin, Lijun Yin, Haoran Xinxiang Vocat & Tech Coll Xinxiang 453006 Henan Peoples R China Yurun Holdings Grp Nanjing 210019 Jiangsu Peoples R China

In order to overcome the low-detection accuracy of traditional methods, an artificial fish swarm algorithm was proposed to detect the energy consumption parameters of green and energy-saving buildings. The type of energy consumption equipment in green and energy-saving buildings is analysed, and the electricity consumption of building energy consumption equipment is taken as the building energy consumption parameter. The hierarchical clustering method was used to establish the classification model of energy consumption parameters, and the energy consumption parameters were classified and processed, and the energy consumption parameters detection model was built, and the preliminary detection results of energy consumption parameters were obtained. The artificial fish swarm algorithm was used to construct the optimisation function of building parameter detection results to obtain the optimal detection results of energy consumption parameters. Experimental results show that the accuracy of the proposed method is between 92.76% and 98.75%, and the practical application effect is good.

关键词： artificial fish swarm algorithm green energy saving building energy consumption parameters hierarchical clustering

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The artificial fish swarm algorithm Improved by Fireworks algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2022年第4期56卷 311-323页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Li, Hongbo Tianjin Univ Commerce Informat Engn Coll Tianjin 300134 Peoples R China Tianjin Univ Commerce Econ Coll Tianjin 300134 Peoples R China

The artificial fish swarm algorithm can achieve good convergence effects in the early stage, but in the late stage, the algorithm has the problems of slow convergence speed and low optimization accuracy, and it is easy to fall into local extremes, making the algorithm's convergence effect poor. Therefore, the characteristics of fireworks algorithm are used to improve the deficiency of fish swarm algorithm that is easy to fall into local extreme value in the late stage, and FWA-artificial fish swarm algorithm is put forward. When the effect of artificial fish swarm algorithm is poor, the explosion, mutation, mapping, and selection operations of fireworks algorithm are introduced to increase the variability of artificial fish, so as to enhance the optimization speed and ability of the algorithm. Finally, the improved algorithm is tested by four typical complex functions which are difficult to find the optimal solution by traditional method. Simulation results prove that the algorithm has the advantages of faster optimization speed, higher precision, and stronger stability.

关键词： artificial fish swarm algorithm fireworks algorithm FWA-artificial fish swarm algorithm function optimization

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Threshold Segmentation of Magnetic Column Defect Image based on artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第6期13卷 502-508页

作者： Wang Jun Hou Mengjie Zhang Ruiran Xiao Jingjing Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou Peoples R China Jiangxi Appl Technol Vocat Coll Sch Mech & Elect Engn Ganzhou Peoples R China

Aiming at the low efficiency of magnetic column surface defect detection, the vulnerability to human influence, and the insufficient anti-noise performance of existing 2D-OTSU threshold segmentation algorithm, an improved artificial fish swarm algorithm combined with 2D-OTSU algorithm was proposed to improve the accuracy and real-time of magnetic column surface defect detection. Firstly, the weight coefficient was added on the basis of the original 2D-OTSU algorithm, and the distance function was set to optimize the weight coefficient. The objective function was established by combining the inter-class discrete matrix and the intra-class discrete matrix, and the optimal threshold was obtained. Secondly, logistic model was used to optimize the perceptual range and moving step size of the artificial fish swarm algorithm, so as to balance the local and global search ability of the algorithm and improve the convergence speed of the algorithm. Finally, the optimal segmentation threshold is used to segment the image, and compared with other algorithms on four benchmark functions. Experimental results show that the improved algorithm can effectively reduce the time complexity of threshold segmentation and improve the efficiency of the algorithm. At the same time, the segmentation accuracy of the improved algorithm for magnetic column defects reaches 93%, which has good practicability.

关键词： Defect detecting threshold segmentation artificial fish swarm algorithm improved 2D-OTSU algorithm

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AGV Path Planning Based on Improved Hybrid artificial fish swarm algorithm 11

AGV Path Planning Based on Improved Hybrid Artificial Fish S...

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11th Joint International Information Technology and artificial Intelligence Conference, ITAIC 2023

作者： Li, Hui Sun, Zhe Luo, Mingyue School of Electrical and Electronic Engineering Changchun University of Technology ChangChun China Changchun University of Technology Faculty of Mechanical and Electronic Engineering ChangChun China

ISBN: (纸本)9798350333664

To solve the problems of slow convergence speed and many path inflection points in AGV path planning, an AGV path planning method based on improved hybrid artificial fish swarm algorithm(IHAFSA) is proposed. Firstly, the sparrow search algorithm and the artificial fish swarm algorithm are used to jointly construct the initial population to improve the population diversity of the algorithm;secondly, adaptive step size, field of view and crowding factor are added to the algorithm to balance the global and local search ability of the algorithm;finally, the fitness function is used to traverse the locally optimal solutions within the field of view, and the resulting locally optimal solutions are used to improve the traditional prey behavior and to exclude the interference of the suboptimal solutions. The results show that the algorithm has good planning results in terms of convergence speed, number of path inflection points and total path length. © 2023 IEEE.

关键词： adaptive AGV artificial fish swarm algorithm path planning sparrow search algorithm

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