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A novel artificial bee colony algorithm with Powell's method

APPLIED SOFT COMPUTING 2013年第9期13卷 3763-3775页

作者： Gao, Wei-feng Liu, San-yang Huang, Ling-ling Xidian Univ Sch Sci Xian 710071 Shannxi Peoples R China China Univ Petr Sch Sci Qingdao 266580 Peoples R China

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 ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Evolutionary algorithms artificial bee colony algorithm Search equation Powell's method

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Prediction of stratum deformation during the excavation of a foundation pit in composite formation based on the artificial bee colony-back-propagation model

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ENGINEERING OPTIMIZATION 2022年第7期54卷 1217-1235页

作者： Feng, Tugen Wang, Chaoran Zhang, Jian Zhou, Kun Qiao, Guangxuan Hohai Univ Key Lab Minist Educ Geomech & Embankment Engn Nanjing Peoples R China

A method for predicting deformation during the excavation of a foundation pit in composite formation is proposed. The artificial bee colony algorithm (ABC) is introduced to optimize the back-propagation (BP) neural network with the input variables filtered. This method is applied to predict the deformation of a foundation pit project. The prediction results are verified by comparing the results with those of other neural network models. The results indicate that the depth of excavation, speed of excavation, friction angle in the soil, gravity, elastic modulus and number of internal support layers are the main factors affecting the deformation of the soil layer around the foundation pit. The ABC algorithm is capable of searching for better solutions of initial weights and thresholds. The ABC-BP model with a 6-12-2 network structure has high prediction accuracy and the best generalization ability.

关键词： Composite formation foundation pit deformation prediction artificial bee colony algorithm BP neural network grey relational analysis

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A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem

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APPLIED SOFT COMPUTING 2013年第3期13卷 1459-1463页

作者： Liu, Yan-Feng Liu, San-Yang Xidian Univ Dept Math Xian 710071 Peoples R China

To minimize the makespan in permutation flowshop scheduling problems, a hybrid discrete artificial bee colony (HDABC) algorithm is presented. In the HDABC, each solution to the problem is called a food source and represented by a discrete job permutation. First, the initial population with certain quality and diversity is generated from Greedy Randomized Adaptive Search Procedure (GRASP) based on Nawaz-Enscore-Ham (NEH) heuristics. Second, the discrete operators and algorithm, such as insert, swap, path relinking and GRASP are applied to generate new solution for the employed bees, onlookers and scouts. Moreover, local search is applied to the best one. The presented algorithm is tested on scheduling problem benchmarks. Experimental results show its efficiency. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Scheduling Permutation flowshop scheduling artificial bee colony algorithm GRASP NEH heuristics

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Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2013年第2期26卷 677-684页

作者： Karaboga, Nurhan Latifoglu, Fatma Erciyes Univ Fac Engn Elect & Elect Eng Dept Kayseri Turkey Erciyes Univ Biomed Eng Dept Fac Engn Kayseri Turkey

Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm PSO algorithm DE algorithm Adaptive IIR filter Noise elimination Transcranial Doppler signal

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Optimal design of damping controllers using a new hybrid artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期52卷 42-54页

作者： Eslami, Mandiyeh Shareef, Hussain Khajehzadeh, Mohammad Natl Univ Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Islamic Azad Univ Anar Branch Dept Civil Engn Anar Iran

This paper integrates the artificial bee colony (ABC) algorithm with the sequential quadratic programming (SQP) to create the new hybrid optimization algorithm, ABC-SQP, for solving global optimization problems and damping of low frequency oscillations in power system stability analyses. The new algorithm combines the global exploration ability of ABC to converge rapidly to a near optimum solution and the accurate local exploitation ability of SQP to accelerate the search process and find an accurate solution. A set of well-known benchmark optimization problems is used to validate the performance of the ABC-SQP as a global optimization algorithm and to facilitate a comparison with the classical ABC. Numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions. Power system stabilizers and supplementary static VAR compensator controllers are designed for two-area-four-machine and five-area-sixteen-machine systems to illustrate the feasibility and effectiveness of the new method in power systems. The performance of the proposed ABC-SQP algorithm is compared with the classic ABC and the genetic algorithm (GA) through eigenvalue analysis and nonlinear time-domain simulation. The simulation results indicate that the controllers designed by the ABC-SQP perform better than those designed by ABC and GA. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Power system stabilizer Static VAR compensator Low-frequency oscillation Hybrid algorithm artificial bee colony algorithm Sequential quadratic programming

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DDoS attack detection based on global unbiased search strategy bee colony algorithm and artificial neural network

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2019年第5期11卷 584-593页

作者： Tian, Qiuting Han, Dezhi Du, Zhenxin Shanghai Maritime Univ Coll Informat Engn 1550 Haigang Ave Shanghai Peoples R China

Distributed denial of service (DDoS) attacks are one of the common cyber threats today and are difficult to trace and prevent. The DDoS attack detection method for a single artificial neural network has the problems of slow convergence speed and easy to fall into local optimum. A DDoS attack detection method combining global unbiased search strategy bee colony algorithm and artificial neural network is proposed. This method uses the loss function of the artificial neural network as the objective function of the global unbiased search strategy bee colony algorithm. The optimal weights and thresholds are chosen as the initialisation parameters of the artificial neural network, in order to avoid the artificial neural network falling into a slow convergence speed and local optimum, thereby realising efficient DDoS attack detection. Experimental results show that the DDoS attack detection method has improved the detection accuracy, convergence speed and has good generalisation ability.

关键词： artificial neural network ANN artificial bee colony algorithm ABC distributed denial of service DDoS loss function convergence speed attack detection

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A modified artificial bee colony algorithm with its applications in signal processing

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2-3期47卷 297-303页

作者： Yu, Xiangyu Zhu, Zucong South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Guangdong Peoples R China Guangdong Co Ltd China Mobile Grp Corp Maoming Branch Maoming Guangdong Peoples R China

artificial bee colony (ABC) algorithm is a biological-inspired optimisation algorithm proposed in recent years. It has been shown to have some advantages than most of conventional biological-inspired algorithms and has been widely used in many applications. However, the ABC algorithm does not consider the balance between global best and local best, and make ABC algorithm insufficiency. In this paper, a modified ABC algorithm is proposed, global best is introduced into the original ABC algorithm to modify the update equation of employed and onlooker bees while the equation for scouts remain unchanged. The effectiveness of the proposed approach is verified on the problem of peak-to-average power ratio reduction in orthogonal frequency division multiplexing signals and multi-level image segmentation. Simulation results showed that the proposed approach has better performance than traditional ABC algorithm with the same computational complexity.

关键词： artificial bee colony algorithm biological-inspired optimisation algorithm peak-to-average power ratio reduction partial transmit sequence image segmentation

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An artificial bee colony optimization-based approach for sizing and composition of Arctic offshore drilling support fleets considering cost-efficiency

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SHIP TECHNOLOGY RESEARCH 2022年第2期69卷 65-88页

作者： Kondratenko, A. A. Bergstrom, M. Suominen, M. Kujala, P. Aalto Univ Sch Engn Dept Mech Engn POB 15300 FI-00076 Aalto Finland

This article presents an optimization-based approach for sizing and composition of an Arctic offshore drilling support fleet considering cost-efficiency. The approach studies the main types of duties related to Arctic offshore drillings: supply, towing, anchor handling, standby, oil spill response, firefighting, and ice management. The approach considers the combined effect of the expected costs of accidental events, the versatility of individual support vessels, and ice management. The approach applies an artificial bee colony algorithm-based optimization procedure. As demonstrated through case studies, the approach may help to find a range of cost-efficient fleet compositions. Some of the obtained solutions are similar to corresponding real-life fleets, indicating that the approach works in principle. Sensitivity analyses indicate that the consideration of the expected costs from accidental events significantly impacts the obtained solution, and that investments to reduce these costs may improve the overall cost-efficiency of an Arctic offshore drilling support fleet.

关键词： Arctic offshore support fleet ice class ships goal-based design fleet optimization artificial bee colony algorithm cost-efficiency metaheuristic

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Network Security Situation Prediction Method Based on Support Vector Machine Optimized by artificial bee colony algorithms

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电脑学刊 2021年第1期32卷 144-153页

作者： Gang Ke Ruey-Shun Chen Yeh-Cheng Chen Jyh-haw Yeh

The validity and accuracy of the network security situation prediction algorithm is of great significance to network security. Aiming at the shortcomings of the basic artificial bee colony algorithm, such as easy to fall into the local optimal solution and slow convergence in the late stage of the algorithm, this paper proposes a network security situation prediction model based on support vector machine(SVM) optimized by improved artificial bee colony algorithm(I-ABC), using I-ABC algorithm for SVM. The penalty factor a and the kernel parameter b are optimized. Finally, the simulation test is performed using real network security situation data. The simulation results show that the proposed algorithm can accurately track the change of situation value and effectively improve the prediction accuracy of network security situation.

关键词： artificial bee colony algorithm support vector machine network security situation prediction

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Application of artificial bee colony algorithm to topology optimization for dynamic stiffness problems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2013年第10期66卷 1879-1891页

作者： Park, Ji-Yong Han, Seog-Young Hanyang Univ Grad Sch Dept Mech Engn Seoul 133791 South Korea Hanyang Univ Sch Mech Engn Seoul 133791 South Korea

The artificial bee colony algorithm (ABCA) was first adopted in topology optimization for dynamic problems. The objective was to obtain a structure with the highest fundamental natural frequency in a certain amount of material, based on the contributed structural sensitivity of each element calculated by the waggle index and eigenvalue. The waggle index update rule, evaluation method of fitness values, and changing filtering size scheme are suggested for obtaining a stable and robust optimal topology based on the ABCA. Examples are provided to examine the applicability and effectiveness of the ABCA compared to bi-directional evolutionary structural optimization (BESO). The following conclusions are obtained through the results of examples based on the ABCA;(1) the ABCA, using the three suggested methods, is very applicable and effective in topology optimization for obtaining a stable and robust optimal layout. (2) It is found that the natural frequencies of the ABCA are always higher than those of the BESO, and average convergence rates of the ABCA are similar or faster than those of the BESO. (3) The optimal topology from the ABCA is nearly obtained in a half stage of the convergence iteration, since volume constraint is applied from the beginning. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Topology optimization Natural frequency Stochastic search method Finite element method

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