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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Jiang, Jingchen Deng, Fang Shi, Xiang Cai, Yeyun Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Chongqing Innovat Ctr Beijing Inst Technol Chongqing 401120 Peoples R China

ISBN: (纸本)9781713872344

The thermoelectric generator is an essential device used in the process of thermoelec-tric power generation. This paper researches the energy optimization problem of thermoelectric generators, and the goal is to maximize total output energy over a duration of time. The Multi-Step Load Optimization (MSLO) method is developed for addressing the problem. In our proposed method, we use artificial bee colony ( ABC) algorithm to search for the resistance values of multi-step to find a high-quality resistance sequence swiftly. Next, the encoding scheme and local search operators are developed. Instead of using the simulation model directly, we use the Kriging model as evaluation function of ABC algorithm to speed up the calculation of the evaluation value. Results show that the kriging model has high efficiency and accuracy, and when compared to state-of-art maximum power point tracking (MPPT) algorithms, the MSLO method has better optimization performance. Copyright (c) 2023 The Authors.

关键词： Thermoelectric generator Maximize output energy Multi step load optimization artificial bee colony algorithm Kriging model

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Network reconstruction from betweenness centrality by artificial bee colony

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Lozano, Manuel Rodriguez, Francisco J. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

Reconstructing complex network structures from measurable data has become a central issue in contemporary network science and engineering. In this paper, we tackle the reconstruction of the network topology from a set of target betweenness centrality values. This metric evaluates the participation of the vertices in the communication along the shortest paths of the network and it has been widely used as centrality measure in analyzing social networks. In this case, the network reconstruction task is formulated as an optimization problem whose search space consists of all possible subsets of edges linking a given set of vertices. It should be noted that the development of optimization algorithms for tackling combinatorial problems involving betweenness centrality is very challenging, because this measure is notoriously expensive to compute. That is why we have made two innovative design decisions (not explored previously in the literature): firstly, to address the problem by proposing an artificial bee colony algorithm, which is a potent swarm intelligence metaheuristic inspired in the foraging behavior of honeybees and secondly, to incorporate in this proposal recent betweenness centrality update techniques that substantially reduce the number of shortest paths which should be re-computed when a network is changed. Extensive experiments verify that our optimizer can achieve better solution quality than the state-ofthe-art metaheuristic for this complex optimization problem and other competing algorithms. We have completed the study of the proposal by evaluating its behavior as tool to reconstruct network topology.

关键词： Network reconstruction artificial bee colony algorithm Betweenness centrality Social networks Network topology Metaheuristics

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Solving Traveling Salesman Problem by Using Combinatorial artificial bee colony algorithms

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INTERNATIONAL JOURNAL ON artificial INTELLIGENCE TOOLS 2019年第1期28卷

作者： Karaboga, Dervis Gorkemli, Beyza Erciyes Univ Comp Engn Dept TR-38700 Kayseri Melikgazi Turkey

artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.

关键词： Combinatorial optimization TSP artificial bee colony algorithm combinatorial artificial bee colony algorithm

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Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach

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APPLIED SOFT COMPUTING 2021年 105卷

作者： Shiue, Yeou-Ren You, Gui-Rong Su, Chao-Ton Chen, Hua Fujian Business Univ Dept Management Engn Fuzhou 350012 Peoples R China Fujian Business Univ Informat Technol Ctr Fuzhou 350012 Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Engn Management 101Sect 2Kuang Fu Rd Hsinchu Taiwan

In ensemble learning, it is necessary to build a balancing mechanism to balance the accuracy of individual learners with the diversity between individual learners to achieve excellent ensemble learning performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, an ensemble learning approach based on balanced accuracy and diversity (ELBAD) that uses a two-phase artificial bee colony (ABC) algorithm is proposed to balance the accuracy and diversity of ensemble learners. In the first phase, the ABC algorithm is used to generate an ensemble classifier with appropriate diversity. In the second phase, the ABC algorithm is used to generate a weighted ensemble classifier. The ELBAD ensemble learning algorithm is significantly superior to other state-of-the-art popular ensemble learning algorithms, including AdaBoost, Bagging, Decorate, extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), and rotation forest (RoF) on 30 UCI datasets. In addition, this study proposes a systematic parameter tuning procedure for the ELBAD algorithm that reduces the time required to generate an ensemble classifier. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Machine learning Ensemble learning Ensemble diversity artificial bee colony algorithm Weighted ensemble

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A Discrete Dynamic artificial bee colony with Hyper-Scout for RESTful web service API test suite generation

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APPLIED SOFT COMPUTING 2021年 104卷 107246-107246页

作者： Sahin, Omur Akay, Bahriye Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

Microservices have become quite important because they decompose an application into a collection of highly maintainable services and reduce the complexity of monolithic architecture. Although the most common method of microservice development is the REST architecture, the number of studies on the automatic testing of RESTful APIs are very limited. For automatic testing of RESTful APIs, different algorithms have been proposed. However, their performance depends on the type of problem being addressed. This study implements the basic artificial bee colony (ABC) algorithm for automatic testing of RESTful web services, and addresses some shortcomings for the RESTful test generation. To overcome these shortcomings, we propose a Discrete Dynamic artificial bee colony with Hyper-Scout (DABC-HS) algorithm for the service-oriented problem. We enhanced the exploration ability of the algorithm by integrating a hyper-scout unit. The presence of solutions that correspond to covered targets but are not related to the remaining targets causes overburdening of the search. In the proposed approach, the onlooker bee phase generates a pool of all solutions and their mutants and applies a dominance based selection operator to select high-quality solutions related to uncovered targets. The experimental results demonstrate that the DABC-HS recorded the highest performance in four of the seven problems while demonstrating similarly good performance in other problems. MOSA, which is the powerful algorithm in the literature, was the best in two of seven problems while ABC algorithm achieved the best performance in one problem. Although the method could not achieve the best result in all problems, it is more successful than the other published methods in the literature. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Test suite generation RESTful artificial bee colony algorithm

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artificial bee colony-based Routing for Mobile Agents on the Internet of Things

Artificial Bee Colony-based Routing for Mobile Agents on the...

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IEEE Electric Power and Energy Conference (EPEC)

作者： Yousefi, Shamim Derakhshan, Farnaz Karimipour, Hadis Univ Tabriz Dept Elec & Comp Engn Tabriz EA Iran Univ Guelph Sch Engn Guelph ON Canada

ISBN: (纸本)9781728164892

In the critical infrastructure of the Internet of Things (IoT), the most efficient approach for data collection is to employ the software Mobile Agents (MAs). In MA-based data collection, the energy exhaustion of wireless communications is much more than the data processing costs. Hence, a crucial challenge in IoT is to offer optimal routing for MAs to save the energy of devices. In this paper, we present a new routing method for MAs on IoT using the artificial bee colony (ABC) algorithm. In our approach, the Euclidean distance between sensor/devices, residual energy, and data priority are considered to measure the appropriateness of the generated routing. Simulation analysis demonstrates that our proposed method improves the energy exhaustion, lifetime, transmission delay, and reliability of IoT.

关键词： artificial bee colony algorithm Data collection Internet of Things Mobile agents Routing

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Application of an Optimized Support Vector Regression algorithm in Short-Term Traffic Flow Prediction

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第6期18卷 719-728页

作者： Ai, Ruibo Li, Cheng Li, Na Qiqihar Univ Coll Sci Qiqihar Peoples R China Qiqihar Univ Coll Comp & Control Engn Qiqihar Peoples R China Qiqihar Univ Publ Foreign Language Teaching & Res Dept Qiqihar Peoples R China

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimiza-tion SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

关键词： artificial bee colony algorithm Optimization Prediction algorithm Short -time Traffic Flow Support Vector Regression

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Research on Area Coverage and Monitoring algorithm of Substation Intelligent Inspection Robot 11

Research on Area Coverage and Monitoring Algorithm of Substa...

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

作者： Tian, Jinsu Ding, Lishun Fan, Yingle Shi, Xiaozhen College of Energy and Electrical Engineering Hohai University Jiangsu China State Grid Xuzhou Power Supply Company Jiangsu China

ISBN: (纸本)9798350333664

As an important part of the power grid, the development of social economy has put forward higher requirements for the safe operation of the power grid, and put forward new challenges in the safety of operation and the timeliness of fault handling. As the development trend of intelligent inspection technology, this paper firstly realizes the scene virtualization in substation by using particle swarm optimization algorithm, and divides the molecular area reasonably. The improved artificial bee colony algorithm is used to cover the subregions. On the basis of the four-color algorithm, the reasonable placement of the surplus robot enhances the emergency response ability of the system, improves the robustness and robustness of the system, and realizes the all-round monitoring of the substation scene. © 2023 IEEE.

关键词： artificial bee colony algorithm Four-color algorithm Intelligent inspection Particle swarm optimization substation

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Determining Lightning Vulnerability of Corner Points of Tall Buildings by Evaluating Their Relevant Risky Regions

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第3期47卷 2825-2834页

作者： Yahyaabadi, Mostafa Aslani, Farshad Vahidi, Behrooz Sadeghi, Mohsen Power Generat Co Esfahan Iran Islamic Azad Univ Khomeinishahr Dept Elect Engn Esfahan Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Naghshejahan Higher Educ Inst Dept Elect Engn Esfahan Iran

Since the lightning escape from the protection system could cause severe damages to the structures and expensive equipment, lightning protection systems should be designed in such a way that the probability of direct lightning strikes can be minimized. The aim of this paper is to model and visualize the weaknesses of tall building's air termination systems against direct lightning strikes. Hence, in this paper, a numerical method is developed based on the combination of artificial bee colony algorithm, leader progression model and charge simulation method. In the proposed approach, three-dimensional modeling of lightning performance is conducted in the problem space and such features of an intelligent meta-heuristic algorithm are incorporated into the problem that let the solution space to be searched intelligently. Therefore, the lightning vulnerability of the corner points of the tall buildings is estimated and the riskiest lightning regions corresponding to each vulnerable point are determined on the simulation-starting surface on buildings. In comparison with the time-consuming conventional method, the proposed approach is also efficient in decreasing the time required to identify protection system weaknesses. A sample real structure including three tall adjacent buildings located in Kuala Lumpur is analyzed as the problem case study and the obtained results are compared with that of previous reported methods. The simulation results verify the potential of the introduced method and depict that optimization intelligent algorithms are able to achieve high-quality solutions in the face of this problem.

关键词： Lightning Air terminal Leader progression model artificial bee colony algorithm

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System Management Model Based on ABC-ACO algorithm under the Background of Digital Economy 1

System Management Model Based on ABC-ACO Algorithm under the...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Fu, Yong College of Economics Sichuan Agricultural University Sichuan Chengdu China

ISBN: (纸本)9798350316469

With the rapid development of the digital economy, the need to manage complex systems is increasing day by day, and traditional management methods are often unable to fully adapt to this challenge. Therefore, this research proposes a system management model based on the ABC-ACO algorithm, which aims to optimize decision-making and resource allocation and improve system performance and efficiency. In this paper, the ABC-ACO algorithm is used as the main optimization tool. The algorithm combines artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO), and has the characteristics of adaptive search and iterative optimization. Through experimental tests, the accuracy rate of the algorithm in this paper is 92% at the highest and 86% at the lowest. The system management model based on the ABC- ACO algorithm performs well in the context of the digital economy. By optimizing decision-making and resource allocation, the model can improve the performance and efficiency of the system. © 2023 IEEE.

关键词： ABC-AOC algorithm Ant colony Optimization algorithm artificial bee colony algorithm Digital Economy

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