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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： Adolfo R. Lopez Christopher J. Cortés Carlos A. Muñoz Luis A. Alonso Gilberto Muñoz M. G. Vazquez José M. Sosa Susana De León Aldaco J. Aguayo Laboratory of Electrical and Power Electronics Tecnológico Nacional de México/ITS de Irapuato Irapuato Mexico Electronics Department Tecnológico Nacional de México/CENIDET Cuernavaca Mexico

ISBN: (纸本)9781665458931

This paper explores the use of the artificial bee colony algorithm to obtain the switching angles of a five-level Cascaded H-bridge Multilevel Inverter. The objective of the algorithm is to reduce the Total Harmonic Distortion in the inverter output voltage. These switching angles are compared in a laboratory prototype with the fsolve method, which is included in the Matlab software. The results show a better Total Harmonic Distortion of the output voltage compared with the fsolve method.

关键词： Total harmonic distortion Software algorithms Prototypes Voltage Switches artificial bee colony algorithm Multilevel inverters

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A metaheuristic multi-objective optimization method for dynamical network biomarker identification as pre-disease stage signal

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APPLIED SOFT COMPUTING 2021年 109卷 107544-107544页

作者： Coleto-Alcudia, Veredas Vega-Rodriguez, Miguel A. Univ Extremadura Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain

Deciphering the signals that are attached to the transition from normal to disease stage is crucial in preventive medicine to understand the progression of complex diseases. Between normal and disease stages there exists the pre-disease stage, in which the disease is yet reversible towards the normal stage. Traditionally, molecular biomarkers have been used to identify the pre-disease stage. However, they have limitations because they have an individual and static nature. In complex diseases, the dynamics and interplays of certain genes have to be taken into account in order to identify the pre-disease stage. Therefore, in complex diseases, it is necessary to use dynamical network biomarkers (DNBs). The development of time-course omics data has been crucial to the use of DNBs as biomarker. In this article, a new two-step method is proposed for the identification of DNBs as pre-disease stage signal. In the first step, the relevant genes in the dataset are pre-filtered using a differential gene expression analysis. In the second step, the DNBs are identified, from a multi-objective optimization viewpoint, by using an artificial bee colony based on Dominance (ABCD) algorithm. Specifically, identified DNBs optimize three objectives: they are the smallest gene network that shows the strongest signal in the earliest time-point of the disease progression and best correlates with the disease phenotype. The proposed method has been evaluated with five time-course microarray datasets and the results have been compared with five methods from other authors, surpassing their results. The effectiveness of the proposed method has been also proved with a leave-one-out cross-validation and a Gene Ontology term enrichment. In fact, the proposed method obtains values around 90% for accuracy, precision, recall, and F1 scores. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Multi-objective optimization artificial bee colony algorithm Pre-filtering Dynamical network biomarker Pre-disease stage

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Two approaches for the min-degree constrained minimum spanning tree problem

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APPLIED SOFT COMPUTING 2021年 111卷 107715-107715页

作者： Ghoshal, Sudishna Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

Given an undirected, connected and weighted graph, the min-degree constrained minimum spanning tree (md_MST) problem aims to find a spanning tree (T) with minimum cost subject to each non-leaf vertex in T must have at least d degree, where d is a constant positive integer. Being a NP-hard problem, it finds several practical applications in the design of networks. In this paper, we present two approaches for this problem in which the first approach is a hybrid metaheuristic technique (hABC) combining an artificial bee colony algorithm with local search, and the second approach is iterated local search (ILS). The proposed hABC distinguishes itself from the existing hybrid artificial bee colony algorithm particularly by its size of employed bee population, initial solution generation, neighborhood operators, the way scout bee phase is handled and the local search. The proposed hABC works with a population of only 3 solutions that effectively coordinates with other key components of hABC and helps in finding new high quality solutions particularly for larger size of available benchmark instances. Extensive experiments on four data-sets of 105 benchmark instances demonstrate that ILS and hABC dominate state-of-the-art approaches. Specifically, hABC finds new best values for 41 instances, whereas ILS finds new best values for 28 instances in comparison to state-of-theart approaches. Experimental results also show that hABC without local search (hABC-LS) is overall superior to state-of-the-art approaches except one where hABC-LS is comparable, but computationally faster on available benchmark instances. (C) 2021 Published by Elsevier B.V.

关键词： Min-degree constrained minimum spanning tree artificial bee colony algorithm Iterated local search Local search Combinatorial optimization problem

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A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization

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APPLIED SOFT COMPUTING 2018年 70卷 826-844页

作者： Badem, Hasan Basturk, Alper Caliskan, Abdullah Yuksel, Mehmet Emin Kahramanmaras Sutcu Imam Univ Dept Comp Engn Kahramanmaras Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey Iskenderun Tech Univ Dept Biomed Engn Antakya Turkey Erciyes Univ Dept Biomed Engn Kayseri Turkey

In this paper, a new optimization method, which is developed especially for optimization of functions with a large number of local minima, is presented. The proposed method is a hybrid optimization algorithm which employs the artificial bee colony (ABC) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithms for combining their powerful features. The most prominent feature of the proposed method over other methods is that it provides accurate results and valuable convergence speeds, as well as easy implementation at the same time. Extensive simulation results supported by detailed statistical analyses show that the proposed method can be used for efficient optimization of functions including well-known benchmark functions and CEC2016 competition functions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm L-BEGS Global optimization Swarm intelligence

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The forecasting of air transport passenger demands in Turkey by using novel meta-heuristic algorithms

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第16期33卷 e6263-e6263页

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Engn Fac Dept Civil Engn TR-71451 Kirikkale Turkey

The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination algorithm (FPA), artificial bee colony algorithm (ABC), Crow Search algorithm (CSA), Krill Herd algorithm (KH), and the Butterfly Optimization algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.

关键词： air transport passenger demand artificial bee colony algorithm butterfly optimization algorithm crow search algorithm flower pollination algorithm krill herd algorithm

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Optimizing thermal load and power scaling in Er³⁺-doped ZBLAN fiber lasers

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OPTICAL FIBER TECHNOLOGY 2021年 64卷 102507-102507页

作者： Gursel, Amira Tandirovic Kilic, Fatih Adana Alparslan Turkes Sci & Technol Univ Elect & Elect Engn Dept Adana Turkey Adana Alparslan Turkes Sci & Technol Univ Comp Engn Dept Adana Turkey

Thanks to characteristics such as high beam qualities and high average powers, largely founded on the outstanding characteristics of fibers as an active medium, fiber lasers have become one of the most popular laser technologies. Although most of the advantages of fiber lasers stem from their geometry, the same geometry is the main source of the limitations. In this study, we focused on managing the thermal load by variable absorption coefficient in order to increase the thermal limits, which are an important limitation of further development in fiber technology. Metaheuristic optimization approaches can be used to improve the performance of fiber technologies with physical constraints. The main aim of metaheuristic optimization is to improve the output of the function with respect to the parameters considered as significant. Swarm Optimization approach is one of the powerful metaheuristic approaches proven to be quite promising to address the above issues. In this study, the optimization of the thermal management of the Er3+-doped ZBLAN fiber laser is performed using artificial bee colony algorithm (ABC). Additionally, this paper proposes four new strategies of initial parameter setting, repair parameter values, and fitness function for the thermal load of the Er3+-doped ZBLAN fiber laser with respect to the output power. The results show that the absorption coefficient functions obtained by ABC algorithm give more effective and efficient results than the obtained rising functions.

关键词： Fiber lasers Thermal management Power scaling artificial bee colony algorithm Numerical function optimization

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BMNABC: Binary Multi-Neighborhood artificial bee colony for High-Dimensional Discrete Optimization Problems

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CYBERNETICS AND SYSTEMS 2018年第7-8期49卷 452-474页

作者： Beheshti, Zahra Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

Many meta-heuristic algorithms have been proposed to solve continuous optimization problems. Hence, researchers have applied various techniques to change these algorithms for discrete search spaces. artificial bee colony (ABC) algorithm is one of the well-known algorithms for real search spaces. ABC has a good ability in exploration but it is weak in exploitation. Several binary versions of ABC have been proposed so far. Since the methods are based on the standard ABC, they have the disadvantage of ABC. In this article, a new binary ABC called binary multi-neighborhood ABC (BMNABC) has been introduced to enhance the exploration and exploitation abilities in the phases of ABC. BMNABC applies the near and far neighborhood information with a new probability function in the first and second phases. A more conscious search than the standard ABC is done in the third phase for those solutions which have been not improved in the previous phases. The performance of algorithm has been evaluated by low- and high-dimensional functions and the 0-1 multidimensional knapsack problems. The proposed method has been compared with state-of-the-art algorithms. The results showed that BMNABC had a better performance in terms of solution accuracy and convergence speed.

关键词： artificial bee colony algorithm binary search space exploitation exploration high-dimensional multi-neighborhood

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An improved artificial bee colony with modified augmented Lagrangian for constrained optimization

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SOFT COMPUTING 2018年第14期22卷 4789-4810页

作者： Long, Wen Liang, Ximing Cai, Shaohong Jiao, Jianjun Zhang, Wenzhuan Guizhou Univ Finance & Econ Guizhou Key Lab Econ Syst Simulat Guiyang 550025 Guizhou Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100083 Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China

artificial bee colony (ABC) algorithm has been successfully applied to solve constrained optimization problems (COPs). However, it is noteworthy that when using ABC to deal with COPs, the commonly used constraint-handling technique is the Deb's feasibility-based rules. To our limited knowledge, the present ABC and its variants with augmented Lagrangian (AL) multiplier method have not been found applications to the COPs. In this paper, a novel constrained optimization method, named IABC-MAL, which integrates the benefit of the improved ABC (IABC) algorithm capability for obtaining the global optimum with the modified AL (MAL) method to handle constraints. This paper presents the first effort to integrate ABC algorithm with the AL method. To verify the performance of the proposed IABC-MAL, 24 well-known benchmark test problems at CEC2006, 18 benchmark test problems at CEC2010, and 5 engineering design problems are employed. Experiment results demonstrate that the proposed IABC-MAL algorithm shows better performance in comparison with other state-of-the-art algorithms from the literature.

关键词： Constrained optimization artificial bee colony algorithm Augmented Lagrangian

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Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array

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IET RENEWABLE POWER GENERATION 2018年第16期12卷 1915-1922页

作者： Goud, J. Saikrishna Kalpana, R. Singh, Bhim Kumar, Shailendra Natl Inst Technol Dept Elect & Elect Engn Mangalore Karnataka India Indian Inst Technol Elect Engn Dept Delhi India

This study presents a single current sensor based hybrid maximum power point tracking method to track the global maximum power point (GMPP) of the photovoltaic (PV) array during the mismatch insolation conditions. This method combines the artificial bee colony (ABC) and hill climbing (HC) algorithms to track the GMPP of a PV array. The proposed method uses the HC algorithm to identify the occurrence of mismatch insolation conditions on PV array. During the mismatch insolation conditions, the proposed method scans the battery charging current (I-charge) versus duty cycle (D) characteristics of the power electronic interface circuit to classify the type of shading pattern of P-V curve and also to identify the vicinity of the GMPP. Based on the kind of shading pattern of a P-V curve, the proposed method operates either ABC or HC algorithm to track the GMPP. To improve the convergence speed of the proposed method, the search space of the ABC algorithm is reduced. The proposed method is modelled and simulated in MATLAB software and its performance is validated experimentally for various mismatch insolation conditions.

关键词： maximum power point trackers photovoltaic power systems artificial bee colony algorithm search problems convergence power electronics maximum power point tracking technique artificial bee colony algorithm ABC algorithm hill climbing algorithm HC algorithm mismatch insolation conditions PV array single current sensor based hybrid maximum power point tracking method global maximum power point GMPP photovoltaic array battery charging current duty cycle characteristics power electronic interface circuit shading pattern P-V curve convergence speed search space MATLAB software

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A Survey of Group Intelligence Optimization algorithms

A Survey of Group Intelligence Optimization Algorithms

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Chang, Chunguang Wu, Xi Shenyang Jianzhu Univ Sch Management Shenyang 110168 Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The group intelligent optimization algorithm provides some new ideas for solving many practical problems. These algorithms have stronger robustness and stronger search ability, and are easy to implement in parallel. It is easy to combine with other algorithms to improve the performance of the algorithm and solve complex practical problems. The effect of the experiment is more obvious. In this paper, the improvement and application of particle swarm optimization algorithm, fireworks algorithm and artificial bee colony algorithm in intelligent algorithm are reviewed, and the advantages and disadvantages are analyzed. The future development of intelligent algorithm is prospected.

关键词： Group intelligence algorithm Particle swarm optimization Fireworks algorithm artificial bee colony algorithm

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