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Hybrid harmony search and artificial bee colony algorithm for global optimization problems

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第8期64卷 2621-2634页

作者： Wu, Bin Qian, Cunhua Ni, Weihong Fan, Shuhai Nanjing Univ Technol Dept Ind Engn Nanjing 210009 Jiangsu Peoples R China

Harmony search (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. In order to enhance the accuracy and convergence rate of harmony search, a hybrid harmony search is proposed by incorporating the artificial bee colony algorithm (ABC). The artificial bee colony algorithm is a new swarm intelligence technique inspired by intelligent foraging behavior of honey bees. The ABC and its variants are used to improve harmony memory (HM). To compare and analyze the performance of our proposed hybrid algorithms, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the hybrid algorithms are discussed by a uniform design experiment. Numerical results show that the proposed algorithms can find better solutions when compared to HS and other heuristic algorithms and are powerful search algorithms for various global optimization problems. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Harmony search algorithm artificial bee colony algorithm Uniform design Chaos Particle swarm optimization Global optimization

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An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 1340-1345页

作者： Liao, Xiang Zhou, Jianzhong Zhang, Rui Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

In this paper, we present a novel adaptive artificial bee colony (AABC) algorithm and compare its efficiency with other existing algorithms for long-term dispatch of cascaded hydropower systems. We formulate the long-term economic dispatch of hydropower systems as a complicated nonlinear optimization problem with a group of complex constraints. We analyze the performances of three different values of the control parameter modification rate (MR) in the AABC. We modify the employed bee phase to improve the global optimal capability of the AABC algorithm, and utilize a novel probabilistic method to enhance the search ability of the onlooker bee phase. Furthermore, we change the scout bee phase to avoid local maxima. We demonstrate the performance of the AABC algorithm and compare it with other algorithms using the data from hydropower systems of Three Gorges in China. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch artificial bee colony algorithm Adaptive Constrained optimization Swarm intelligence Cascaded hydropower system

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DisABC: A new artificial bee colony algorithm for binary optimization

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APPLIED SOFT COMPUTING 2012年第1期12卷 342-352页

作者： Kashan, Mina Husseinzadeh Nahavandi, Nasim Kashan, Ali Husseinzadeh Tarbiat Modares Univ Dept Ind Engn Fac Engn Tehran Iran

artificial bee colony (ABC) algorithm is one of the recently proposed swarm intelligence based algorithms for continuous optimization. Therefore it is not possible to use the original ABC algorithm directly to optimize binary structured problems. In this paper we introduce a new version of ABC, called DisABC, which is particularly designed for binary optimization. DisABC uses a new differential expression, which employs a measure of dissimilarity between binary vectors in place of the vector subtraction operator typically used in the original ABC algorithm. Such an expression helps to maintain the major characteristics of the original one and is respondent to the structure of binary optimization problems, too. Similar to original ABC algorithm, DisABC's differential expression works in continuous space while its consequence is used in a two-phase heuristic to construct a complete solution in binary space. Effectiveness of DisABC algorithm is tested on solving the uncapacitated facility location problem (UFLP). A set of 15 benchmark test problem instances of UFLP are adopted from OR-Library and solved by the proposed algorithm. Results are compared with two other state of the art binary optimization algorithms, i.e., binDE and PSO algorithms, in terms of three quality indices. Comparisons indicate that DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Binary optimization artificial bee colony algorithm Swarm intelligence Dissimilarity measure of binary structures Uncapacitated facility location problem

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A comparison of swarm-based optimization algorithms in linear antenna array synthesis

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第4期20卷 1520-1531页

作者： Durmus, Ali Kurban, Rifat Karakose, Ercan Kayseri Univ Vocat Coll Dept Elect & Energy Kayseri Turkey Kayseri Univ Vocat Coll Dept Comp Technol Kayseri Turkey Kayseri Univ Engn & Architecture & Design Fac Dept Nat Sci Kayseri Turkey

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with a low side lobe level (SLL) at a desired half power beam width in far-field. The amplitude and position values of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper, swarm-based metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10-, 16-, 24- and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers, and statistical results show that the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

关键词： Linear antenna array synthesis Side lobe level Particle swarm optimization artificial bee colony algorithm Mayfly algorithm Jellyfish search algorithm

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Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach

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INFORMATION SYSTEMS FRONTIERS 2021年第2期23卷 375-389页

作者： Liu, Qi Feng, Gengzhong Tayi, Giri Kumar Tian, Jun Xi An Jiao Tong Univ Sch Management 28 Xianning Rd Xian 710049 Shaanxi Peoples R China Minist Educ Proc Control & Efficiency Engn Key Lab 28 Xianning Rd Xian 710049 Shaanxi Peoples R China SUNY Albany Sch Business Albany NY 12222 USA

To make informed decisions, managers establish data warehouses that integrate multiple data sources. However, the outcomes of the data warehouse-based decisions are not always satisfactory due to low data quality. Although many studies focused on data quality management, little effort has been made to explore effective data quality control strategies for the data warehouse. In this study, we propose a chance-constrained programming model that determines the optimal strategy for allocating the control resources to mitigate the data quality problems of the data warehouse. We develop a modified artificial bee colony algorithm to solve the model. Our work contributes to the literature on evaluation of data quality problem propagation in data integration process and data quality control on the data sources that make up the data warehouse. We use a data warehouse in the healthcare organization to illustrate the model and the effectiveness of the algorithm.

关键词： Data quality Data warehouse Chance-constrained programming Optimization model artificial bee colony algorithm

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EFFECTIVE CLUSTER-FIRST ROUTE-SECOND APPROACHES USING METAHEURISTIC algorithmS FOR THE CAPACITATED VEHICLE ROUTING PROBLEM

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2021年第1期28卷 14-38页

作者： Comert, Serap Ercan Yazgan, Harun Resit Sakarya Univ Dept Ind Engn Sakarya Turkey

In this paper, three cluster-first route-second approaches are proposed to solve the capacitated vehicle routing problem (CVRP) that extends a traveling salesman problem (TSP). In the first phase, a giant tour covering all customers is built using three different metaheuristic algorithms as an ACO, a GA, and an ABCA. Then, the giant tour is split with respecting the vehicle capacity, and vehicles are loaded. In the second phase, we transform our problem into a small TSP after completing the clustering process, and a routing problem is solved based on a Branch-and-Bound algorithm. We evaluate the performance of these approaches on the benchmark problems. The computational results show that these approaches achieve high-quality results and gain an advantage in terms of CPU time. Besides, these approaches are also applied to a real-life case study related to a distribution CVRP meeting the weekly demands of a supermarket chain and provide a better routing solution.

关键词： cluster-first route-second approach capacitated vehicle routing problem ant colony optimization algorithm genetic algorithm artificial bee colony algorithm

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Performance Analysis of Localization System for Wireless Robotic Capsule Endoscopy Based on 5 DOF 1st

Performance Analysis of Localization System for Wireless Rob...

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1st IFToMM for Sustainable Development Goals workshop (I4SDG)

作者： Suveren, Memduh Kanaan, Muzaffer Erciyes Univ Dept Mechatron Engn TR-38039 Kayseri Turkey

ISBN: (纸本)9783030873837;9783030873820

The wireless robotic capsule endoscopy technique is a relatively painless and invasive medical imaging technique. Most diseases in the gastrointestinal (GI) tract can be diagnosed with robotic capsule endoscopy, even in areas that cannot be reached with conventional colonoscopy. Knowing the position of the robot in robotic capsule endoscopy both speeds up the treatment process and gives the opportunity to control the robot from the outside during the procedure (active capsule endoscopy). In this study, magnetic positioning technique was used to obtain the positions of the robotic capsule in the small intestine model. With a ring-shaped permanent magnet placed around the capsule, the Magnetic Flux Density (MFD) equations were calculated analytically using two different techniques: Biot-Savart and Charge model. The positioning performances of both magnetic models were compared, and the artificial bee colony (ABC) optimization algorithm and the Levenberg-Marquardt (LM) method were used together while calculating the nonlinear equations. As a result, we found that the Charge model was 61% faster than the Biot-Savart model under the same simulation conditions, and the position and angle errors of the Charge model were at least 87% less on average than the BiotSavart model. Under noisy simulation conditions, the performance of the Charge model was observed to be either better or very close to that of Biot-Savart.

关键词： SDG9 Robotic capsule endoscopy In body localization Annular magnet artificial bee colony algorithm Levenberg-Marquardt method

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A global best artificial bee colony algorithm for global optimization

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2012年第11期236卷 2741-2753页

作者： Gao, Weifeng Liu, Sanyang Huang, Lingling Xidian Univ Dept Appl Math Xian 710071 Peoples R China

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in the ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose a modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation. In addition, to enhance the global convergence, when producing the initial population and scout bees, both chaotic systems and opposition-based learning method are employed. Experiments are conducted on a set of 26 benchmark functions. The results demonstrate good performance of ABC/best in solving complex numerical optimization problems when compared with two ABC based algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Initial population Variant artificial bee colony algorithm Search strategy

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An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期37卷 67-77页

作者： Sulaiman, Mohd Herwan Mustafa, Mohd Wazir Shareef, Hussain Abd Khalid, Saiful Nizam Univ Malaysia Perlis Sch Elect Syst Engn Kangar 01000 Perlis Malaysia Univ Teknol Malaysia UTM Fac Elect Engn Skudai Johor 81310 Malaysia Univ Kebangsaan Malaysia Fac Elect Engn & Build Environm Bangi Malaysia

This paper presents a new method for real and reactive power tracing in a deregulated power system by introducing the hybrid artificial bee colony (ABC) algorithm and least squares support vector machine (LS-SVM), namely as ABC-SVM. The idea is to use ABC algorithm to obtain the optimal values of regularization parameter, gamma and Kernel RBF parameter, sigma(2), which are embedded in LS-SVM toolbox and adopt a supervised learning approach to train the LS-SVM model. The technique that uses Superposition method is utilized as a teacher. Based on power flow solution and power tracing procedure by Superposition method, the description of input-output for training and testing data are created. The generators' contributions to real and reactive loads in the test system are expected can be traced accurately by proposed ABC-SVM model. In this paper, IEEE-14 bus system is used to illustrate the effectiveness of the proposed ABC-SVM model compared to that of Superposition method. The comparison with the cross-validation (CV) technique and other hybrid technique to obtain the hyper-parameters also has been presented in this paper. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Least squares support vector machine Superposition method

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Optimum design of a reinforced concrete beam using artificial bee colony algorithm

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COMPUTERS AND CONCRETE 2012年第3期10卷 295-306页

作者： Ozturk, H. T. Durmus, Ay. Durmus, Ah. Karadeniz Tech Univ Dept Civil Engn Trabzon Turkey

Optimum cost design of a simply supported reinforced concrete beam is presented in this paper. In the formulation of the optimum design problem, the height and width of the beam, and reinforcement steel area are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the beam consisting the cost of concrete, steel and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

关键词： optimization metaheuristic artificial bee colony algorithm reinforced concrete beams

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