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

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1597-1613页

作者： Nandhini, S. S. Kannimuthu, S. Bannari Amman Inst Technol Sathyamangalam Tamil Nadu India Karpagam Coll Engn Coimbatore Tamil Nadu India

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 userdefined 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.

关键词： artificial fish swarm algorithm data mining frequent itemset mining high average utility itemsets itemset mining utility mining

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An adaptive stochastic resonance detection method with a knowledge-based improved artificial fish swarm algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第8期81卷 11773-11794页

作者： Hao, Jing Huang, Fuyu Shen, Xuejv Jiang, Chundong Lin, Xiaoran Hebei Univ Econ & Business Sch Informat Technol Shijiazhuang Hebei Peoples R China Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Hebei Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

The selection of the parameters plays a decisive role in the detection performance of parameter- tuning stochastic resonance (SR) system. In order to solve the problems that the existing adaptive stochastic resonance (ASR) system is easy to fall into local optimum and slow convergence rate in parameter optimization, an ASR detection method with knowledge-based improved artificial fish swarm algorithm (AFSA) is proposed. The lens based Opposition-based Learning strategy (lensOBL) is introduced into the AFSA, micro and macro adjustments can be made to enhance the global search ability, especially in the later stage of the algorithm. In addition, in order to speed up the convergence rate of the optimization, the relationship between the system structure parameters and the SR phenomenon produced is considered to guide the optimization process of the algorithm. Compared with the ASR methods based on the AFSA, the proposed method can obtain better parameters and improves the detection performance of the system. Additionally, to solve the problem of detecting multifrequency weak signals submerged in strong noise and many frequency bandwidths, firstly, the SR of the weak signal of large parameters is realized through adjusting the damping coefficient and the system shape parameters, which solves the problem that the traditional SR is only suitable for small parameter weak signal detection. Then, parallel ASR detection systems are designed based on the idea of the proposed method to identify multifrequency weak radio signals. Finally, simulation data and experimental analysis show that, compared with other adaptive parameter-tuning SR methods, the proposed method enhances the convergence speed and accuracy of optimization, has good robustness, and improves the diversity of fish swarm. It enables the optimal detection of multifrequency weak signal better in strong noise background, and broadens the potential applications of weak signal detection methods based on the principle

关键词： Adaptive stochastic resonance Multifrequency weak signal artificial fish swarm algorithm Parameter optimization

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Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第10期46卷 8218-8232页

作者： Cheng, Maolin Xiang, Minyin Suzhou Univ Sci & Technol Sch Math & Phys Suzhou 215009 Peoples R China Huangshan Univ Sch Math & Stat Huangshan Peoples R China

The traditional Cobb-Douglas production function uses the compact mathematical form to describe the relationship between the production results and production factors in the production technology process. However, in macro-economic production, multi-structured production exists universally. In order to better demonstrate such input-output relation, a composite production function model is proposed in this article. In aspect of model parameter estimation, artificial fish swarm algorithm is applied. The algorithm has satisfactory performance in overcoming local extreme value and acquiring global extreme value. Moreover, realization of the algorithm does not need the gradient value of the objective function. For this reason, it is adaptive to searching space. Through the improved artificial fish swarm algorithm, convergence rate and precision are both considerably improved. In aspect of model application, the composite production function model is mainly used to calculate economic growth factor contribution rate. In this article, a relatively more accurate calculating method is proposed. In the end, empirical analysis on economic growth contribution rate of China is implemented.

关键词： artificial fish swarm algorithm Empirical analysis Composite production function Contribution rate

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Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2073-2083页

作者： Ma, Changxi He, Ruichun Lanzhou Jiaotong Univ Sch Traff & Transportat Anning West Rd Lanzhou 730070 Gansu Peoples R China

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system.

关键词： Adaptive genetic algorithm artificial fish swarm algorithm Green wave traffic Control system Optimization

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Computation Scheduling of Multi-Access Edge Networks Based on the artificial fish swarm algorithm

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IEEE ACCESS 2021年 9卷 74674-74683页

作者： Li, Tiantian Yang, Feng Zhang, Depeng Zhai, Linbo Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

With the increasing requirements for computing in modern society, Multi-access Edge Computing (MEC) has received widespread attention for meeting low-latency. In MEC network, mobile devices can offload computing-intensive tasks to edge servers for computing. Wireless Power Transmission (WPT) provides initial energy for mobile devices, and the tasks of mobile devices consume energy when they are locally calculated or completely offloaded. The combination of the two technologies forms the Wireless Powered Mobile Edge Computing (WP-MEC) network. In this article, considering the impact of WPT transmission time tau(0), we study the offloading and scheduling of tasks for multiple mobile devices in the WP-MEC network, which is an NP-hard problem. We formulate this scheduling problem to minimize the time delay under the constraint of WPT transmission energy. We regard our problem studied in this paper as a multidimensional knapsack problem (MKP). The difference is that the knapsack capacity in MKP is limited, while in our problem, the knapsack that one item can choose is limited. Therefore, we improve the artificial fish swarm algorithm (AFSA) and propose Computation Scheduling Based on the artificial fish swarm algorithm (CS-AFSA) to find the optimal scheduling. We encode a scheduling scheme as an artificial fish and regard the delay corresponding to the scheduling as the optimization object. The optimal artificial fish can be gradually approached and determined through the swarm, follow and prey behavior of artificial fish. The optimal artificial fish is the optimal scheduling scheme. More importantly, based on the original behavior of AFSA, we also improve the scheme that does not meet the WPT energy constraint, including the modification of infeasible artificial fish and insufficient artificial fish. Besides, we also consider how to find the best WPT transmission time tau(0). Finally, we perform data simulation on the proposed algorithm.

关键词： artificial fish swarm algorithm WP-MEC network WPT transmission time

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Application of an artificial fish swarm algorithm in Symbolic Regression

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第4期E96D卷 872-885页

作者： Liu, Qing Odaka, Tomohiro Kuroiwa, Jousuke Ogura, Hisakazu Univ Fukui Grad Sch Engn Dept Nucl Power & Energy Safety Engn Fukui 9108501 Japan

An artificial fish swarm algorithm for solving symbolic regression problems is introduced in this paper. In the proposed AFSA, AF individuals represent candidate solutions, which are represented by the gene expression scheme in GEP. For evaluating AF individuals, a penalty-based fitness function, in which the node number of the parse tree is considered to be a constraint, was designed in order to obtain a solution expression that not only fits,the given data well but is also compact. A number of important conceptions are defined, including distance, partners, congestion degree, and feature code. Based on the above concepts, we designed four behaviors, namely, randomly moving behavior, preying behavior, following behavior, and avoiding behavior, and present their respective formalized descriptions. The exhaustive simulation results demonstrate that the proposed algorithm can not only obtain a high-quality solution expression but also provides remarkable robustness and quick convergence.

关键词： artificial fish swarm algorithm symbolic regression parse tree optimization penalty

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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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A pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem

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TRANSPORTMETRICA A-TRANSPORT SCIENCE 2020年第3期16卷 1648-1670页

作者： Liu, Yi Feng, Xuesong Zhang, Lukai Hua, Weixing Li, Kemeng Beijing Jiaotong Univ Sch Traff & Transportat 3 Shangyuancun Beijing 100044 Peoples R China

This study presents a multi-objective optimization model for the urban electric transit network design problem by simultaneously determining the set of transit routes, the service frequency and the location of charging depots with the objectives of minimizing the costs for passengers and operators. The constraints about the bus route, charging depot, vehicle operation and charging schedule are considered to ensure the rational design and operational feasibility of the electric transit network. The solution approach is based on a Pareto artificial fish swarm algorithm (PAFSA) in combination with the crossover and mutation operators. The transit network in an urban region of Beijing is studied in a case study, revealing that the proposed optimization model solved by the PAFSA is able to provide the Pareto optimal solutions to design a relatively large-scaled transit network for the best fits between the passengers and operators.

关键词： Electric transit network design service frequency charging depot location artificial fish swarm algorithm multi-objective optimization

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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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An artificial fish swarm algorithm for the Multicast Routing Problem

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IEICE TRANSACTIONS ON COMMUNICATIONS 2014年第5期E97B卷 996-1011页

作者： Liu, Qing Odaka, Tomohiro Kuroiwa, Jousuke Shirai, Haruhiko Ogura, Hisakazu Univ Fukui Fukui 9108501 Japan

This paper presents an artificial fish swarm algorithm (AFSA) to solve the multicast routing problem, which is abstracted as a Steiner tree problem in graphs. AFSA adopts a 0-1 encoding scheme to represent the artificial fish (AF), which are then subgraphs in the original graph. For evaluating each AF individual, we decode the subgraph into a Steiner tree. Based on the adopted representation of the AF, we design three AF behaviors: randomly moving, preying, and following. These behaviors are organized by a strategy that guides AF individuals to perform certain behaviors according to certain conditions and circumstances. In order to investigate the performance of our algorithm, we implement exhaustive simulation experiments. The results from the experiments indicate that the proposed algorithm outperforms other intelligence algorithms and can obtain the least-cost multicast routing tree in most cases.

关键词： artificial fish swarm algorithm multicast routing Steiner tree problem network optimization

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