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Heuristic Artificial Bee Colony algorithm for Uncovering Community in Complex Networks

MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-12页

作者： Guo, Yuquan Li, Xiongfei Tang, Yufei Li, Jun Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Florida Atlantic Univ Comp & Elect Engn & Comp Sci Dept Boca Raton FL 33431 USA Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China Changchun Univ Sci & Technol Dept Appl Math Changchun 130022 Peoples R China

Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC) algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

关键词： HEURISTIC programming bees algorithm SWARM intelligence SUBGRAPHS MATHEMATICAL analysis

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Inverse design of airfoils via an intelligent hybrid optimization technique

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ENGINEERING WITH COMPUTERS 2017年第3期33卷 361-374页

作者： Tandis, Emad Assareh, Ehsanolah Univ Jundi Shapor Dept Mech Engn Dezful Iran Islamic Azad Univ Dezful Branch Dept Mech Engn Dezful Iran

The solutions for inverse shape design (ISD) problems are provided in many cases using evolutionary algorithms linked up with CFD solvers. Among the optimization methods, evolutionary algorithms have many advantages, especially in stability, but they usually need a large number of function evaluations. This can be more important as evaluation of cost function requires flow solution which is usually a time-consuming process. This paper presents a new population-based hybrid algorithm called genetic-based bees algorithm (GBBA) as a solution to ISD problems. This method uses crossover and neighborhood searching operators derived from, respectively, genetic algorithm (GA) and bees algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Three test cases have been used to compare the performance of the proposed hybrid algorithm with GA and BA. Here, both ideal and viscose flow solvers are involved to solve flow equations in the physical domain. PARSEC and Bezier are two shape definition methods used for surface modification in these cases. The results show that the final shape obtained by the proposed hybrid algorithm is more accurate compared to either BA or GA. Furthermore, speed convergence increases when GBBA is employed.

关键词： Inverse shape design Genetic algorithms bees algorithm Airfoil optimization Hybrid algorithm

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A comparison of nature inspired algorithms for the quadratic assignment problem

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2017年第4期65卷 513-522页

作者： Chmiel, W. Kadluczka, P. Kwiecien, J. Filipowicz, B. AGH Univ Sci & Technol 30 Mickiewicza Ave PL-30059 Krakow Poland

This paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.

关键词： permutation problem quadratic assignment problem ant algorithm bees algorithm

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Self-Adaptive Artificial Bee Colony for Function Optimization

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JOURNAL OF CONTROL SCIENCE AND ENGINEERING 2017年第8-14期2017卷 1-13页

作者： Tang, Mingzhu Long, Wen Wu, Huawei Zhang, Kang Shardt, Yuri A. W. Changsha Univ Sci & Engn Sch Energy & Power Engn Changsha 410114 Hunan Peoples R China Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Guizhou Univ Finance & Econ Guizhou Key Lab Econ Syst Simulat Guiyang 550004 Guizhou Peoples R China Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

Artificial bee colony (ABC) is a novel population-based optimizationmethod, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

关键词： bees algorithm SELF-adaptive software MATHEMATICAL optimization PARAMETERS (Statistics) SET theory

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Using the bees algorithm to select the optimal speed parameters for wind turbine generators

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2012年第1期24卷 17-26页

作者： Fahmy, A. A. Cardiff Univ Mfg Engn Ctr Sch Engn Queens Bldg Cardiff CF24 3AA S Glam Wales

The bees algorithm is a recently developed optimization technique that mimics the foraging behavior of honey bees in nature. This study investigates the use of the bees algorithm for the selection of the optimal operating speed parameters for wind power units. Three speed parameters need to be optimized, namely, the rated, cut-in, and cut-off (furling) speed of the turbine. The aim of the optimization process is to maximize the yearly power yield and turbine usage time. The choice of the best parameters depends from the wind frequency distribution at the site of installation. Eleven locations on the coastal areas of Egypt were chosen as case studies. The well-known Particle Swarm Optimization was used as a control optimization algorithm. A popular classical approach based on the manual optimization of the sole rated speed was used as baseline for the comparison of results. The optimization of all the three speed parameters and the use of intelligent optimization techniques represent the novelties of this paper. The study showed that the bees algorithm outperformed the other two optimization methods. The proposed algorithm was able to find speed parameters that greatly enhanced the power yield, without compromising the usage time or significantly increasing the capital costs. The comparison between the standard manual optimization method and the two intelligent optimization techniques proved the superiority of the latter ones. (C) 2011 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Optimization Wind turbine generator Capacity factor bees algorithm PSO Swarm intelligence

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bees-algorithm-BASED OPTIMIZATION OF COMPONENT SIZE AND CONTROL STRATEGY PARAMETERS FOR PARALLEL HYBRID ELECTRIC VEHICLES

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2012年第7期13卷 1177-1183页

作者： Long, V. T. Nhan, N. V. Nha Trang Univ Fac Mech Engn Khanh Hoa 650000 Vietnam

This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.

关键词： Hybrid electric vehicles (HEV) bees algorithm Intelligent optimization Parallel HEV control strategy

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bees algorithm for Construction of Multiple Test Forms in E-Testing

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2011年第3期4卷 209-221页

作者： Songmuang, Pokpong Ueno, Maomi Waseda Univ Fac Human Sci Tokorozawa Saitama 3591192 Japan Univ Electrocommun Grad Sch Informat Syst Dept Social Intelligence & Informat Chofu Tokyo 1828585 Japan

The purpose of this research is to automatically construct multiple equivalent test forms that have equivalent qualities indicated by test information functions based on item response theory. There has been a trade-off in previous studies between the computational costs and the equivalent qualities of test forms. To alleviate this problem, we propose an automated system of test construction based on the bees algorithm in parallel computing. We demonstrate the effectiveness of the proposed system through various experiments.

关键词： E-testing multiple test forms test construction bees algorithm parallel computing

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Improvement of scatter search using bees algorithm

Improvement of scatter search using Bees Algorithm

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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Ali Makki Sagheer Ahmed T. Sadiq Mohammed S. Ibrahim Computer Science Department College of Computer University of Anbar Iraq Computer Science Department University of Technology Baghdad Iraq

ISBN: (纸本)9781467323925

The Scatter Search is one of the population-based metaheuristics, which can be used to solve a wide range of problems including Traveling Salesman Problem (TSP), Routing Protocol, and End-to-End QoS, while bees algorithm can be applied to optimize the solution of these problems. In this paper, an improved Scatter Search algorithm based on bees algorithm has been presented. The improvement provides Scatter Search with random exploration for search space of problem and more of intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithm produces better performance than original Scatter Search algorithm. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

关键词： component Metaheuristic Scatter Search bees algorithm Combinatorial Problems Traveling Salesman Problem travelling salesman problems Scattering Searches Bee SEARCHING algorithm

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Directed Bee Colony Optimization algorithm to Solve the Nurse Rostering Problem

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第20170101期2017卷 6563498-6563498页

作者： Rajeswari, M. Amudhavel, J. Pothula, Sujatha Dhavachelvan, P. Pondicherry Univ Dept CSE Pondicherry India KL Univ Dept CSE Guntur Andhra Pradesh India

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

关键词： bees algorithm SHIFT systems COMBINATORIAL optimization PROBLEM solving algorithmS

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On the application of bees algorithm to the problem of crack detection of beam-type structures

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COMPUTERS & STRUCTURES 2011年第23-24期89卷 2169-2175页

作者： Moradi, S. Razi, P. Fatahi, L. Shahid Chamran Univ Dept Mech Engn Ahvaz 61355 Iran Dalhousie Univ Dept Civil & Resource Engn Halifax NS B3J 1Z1 Canada

In this paper, the application of the bees algorithm (BA) to the problem of crack detection in beams is introduced. A numerical as well as an experimental study is designed to predict a single open edge-crack in cantilever beams. The crack is modeled by a rotational spring, whose stiffness could be determined by the size of the crack. The weighted sum of the squared errors between the measured and computed natural frequencies is used as the objective function. The results show that both the size and location of the crack can be predicted well through this method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： bees algorithm Crack detection Cantilever beam Particle swarm optimization Modal testing

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