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Automatic Data Clustering Based Mean Best artificial bee colony Algorithm

Computers, Materials & Continua 2021年第8期68卷 1575-1593页

作者： Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer Department Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for artificial Intelligence Technology(CAIT) Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer Sciences Universiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmmanJordan

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** bee colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

关键词： artificial bee colony automatic clustering natural images validity index number of clusters

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MUTUAL artificial bee colony ALGORITHM FOR MOLECULAR DOCKING

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International Journal of Biomathematics 2013年第6期6卷 17-27页

作者： RUIXIN MA XIUJUAN XU LEI ZHAO REN CAO QIANG FANG School of Software Dalian University of Technology Dalian 116620 P. R. China

Molecular docking method plays an important role on the quest of potential drug candidates, which has been proven to be a valuable tool for virtual screening. Molecular docking is commonly referred to as a parameter optimization problem. During the last decade, some optimization algorithms have been introduced, such as Lamarckian genetic algorithm （LGA） and SODOCK embedded in the AutoDock program. On the basis of the latest docking software AutoDock4.2, we present a novel docking program ABCDock, which incorporates mutual artificial bee colony （MutualABC） into AutoDock. Computer simulation results demonstrate that ABCDock takes precedence over AutoDock and SODOCK, in terms of convergence performance, accuracy, and the lowest energy, especially for highly flexible ligands. It is noteworthy that ARCDock yields a higher success rate. Also, in comparison with the other state-of-the-art docking methods, namely GOLD, DOCK and FlexX, ABCDock provides the smallest RMSD in 27 of 37 cases.

关键词： artificial bee colony AutoDock molecular docking

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Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control

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Journal of Southeast University(English Edition) 2014年第4期30卷 449-455页

作者：周霞沈炯李益国东南大学能源与环境学院南京210096 金陵科技学院机电工程学院南京211169

In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is *** the proposed algorithm a novel reference point based preference expression method is *** fitness assignment function is defined based on the nondominated rank and the newly defined preference *** archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the *** experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved *** simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.

关键词： preference multiobjective artificial bee colony superheated steam temperature control optimization

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Improved artificial bee colony Algorithm for Continuous Optimization Problems

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Journal of Computer and Communications 2014年第4期2卷 108-116页

作者： Mustafa Servet Kiran Ahmet Babalik Computer Engineering Department Selcuk UniversityKonyaTurkey

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

关键词： artificial bee colony Selection Mechanism Memory Board Numerical Optimization Energy Estimation

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Distorted Waveform Balancing Using an artificial bee colony (ABC) Based Optimal Control for Mitigating Total Harmonics in Single Phase Inverter

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Circuits and Systems 2016年第9期7卷 2154-2167页

作者： N. M. Spencer Prathap Singh Kesavan Nair T. Ajith Bosco Raj Department of EEE CSI Institute of Technology Thovalai Tamilnadu India Department of ECE PSN College of Engineering and Technology Tirunelveli Tamilnadu India

The main objective of this paper is to reduce the total harmonics in a single phase voltage source inverter using artificial bee colony (ABC) optimization technique for critical load applications. Single phase inverter is a non-linear load using power electronic components causing distortions in the load voltage and current wave patterns from the sinusoidal waveforms due to harmonics. The mapping state space model for a full bridge voltage source inverter was developed using output load resistance. An optimal ABC technique has been designed and optimized values are estimated using a full bridge voltage controlled inverter using Proportional Integral Algorithm. The MATLAB/SIMULINK tool and Experimental setup were implemented and their THD values were estimated. Also this ABC scheme is compared with the previous results such as PI Algorithm, Fuzzy logic controller and Neuro-fuzzy controllers. From the simulation and experimental results using ABC algorithm, it is observed that the total harmonics are mitigated considerably compared to previous results with respect to the power quality standards such as IEEE-519 and IEC 61000.

关键词： Voltage Source Inverter THD Proportional Integral Controller artificial bee colony Voltage Harmonics Current Harmonics

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The Analysis of Peculiar Control Parameters of artificial bee colony Algorithm on the Numerical Optimization Problems

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Journal of Computer and Communications 2014年第4期2卷 127-136页

作者： Mustafa Servet Kiran Mesut Gündüz Computer Engineering Department Selcuk UniversityKonyaTurkey

artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.

关键词： artificial bee colony Effects of the Parameters Parameter Tuning Number of Scout bee Limit Value

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A Discrete artificial bee colony for Distributed Permutation Flowshop Scheduling Problem with Total Flow Time Minimization

A Discrete Artificial Bee Colony for Distributed Permutation...

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第37届中国控制会议

作者： Jia-Qi Pan Wen-Qiang Zou Jun-hua Duan Shengli No.1 Middle School School of Mechatronic Engineering and Automation Shanghai University Computer Center Shanghai University

The distributed permutation flowshop scheduling problem（DPFSP） has important applications in modern *** paper presents a discrete artificial bee colony（DABC） algorithm for solving the DPFSP with total flow time criterion. We use the job permutation based representation to represent the candidate solutions. The insertion operator is adapted to generate neighboring solutions in the search process. We re-design the search phases including employ bees, onlooker bees, and scout bees according to the problem-specific knowledge. A comprehensive computational campaign based on the 720 benchmark instances demonstrates the effectiveness of the proposed DABC algorithm for solving the DPFSP with the total flowtime criterion.

关键词： Scheduling Flowshop artificial bee colony Total Flow Time

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An Improved artificial bee colony Algorithm with History Best Points

An Improved Artificial Bee Colony Algorithm with History Bes...

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第30届中国控制与决策会议

作者： Xingyu Xia Xi Wang Haidong Hu Dongmei Wu Hao Gao The Institute of Advanced Technology Nanjing University of Posts and Telecommunications College of Automation Nanjing University of Posts and Telecommunications Beijing Institute of Control Engineering

Depending on the power global search ability, artificial bee colony algorithm attracts more attentions in recent years. But its slow convergence rate constraints its development. To better balance its exploration and exploitation abilities, we define a new point named as mean history best points（MHB） to lead the direction of bee population. The numerical experiments on the basic benchmark functions validate the efficiency of our algorithm.

关键词： artificial bee colony convergence rate evolutionary computation global search ability

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An improved discrete artificial bee colony algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance

An improved discrete artificial bee colony algorithm for the...

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第三十九届中国控制会议

作者： Jiayang Mao XiaoLu Hu Quan-ke Pa Zhonghua Miao Chuangxin He M Fatih Tasgetiren School of Mechanical and Electrical Engineering and Automation Shanghai University China Rural Technology Development Center International logistics management department Yasar University

The distributed permutation flowshop scheduling problem with preventive maintenance operator（PM/DPFSP） is closely related to modern *** paper presents an improved discrete artificial bee colony（IDABC） algorithm for solving this *** criterion to be optimized is the *** improved NEH heuristic method is proposed to initialize the population *** adapted a local search method with insertion and swap operator to produce neighboring solutions in employ bee phase and onlooker bee phase.A global search method with destruction and reconstruction phase is introduced to avoid local optima in scout bee *** parameters for the proposed IDABC are calibrated by means of a design of experiments and analysis of *** conduct extensive experiments to test the performance of *** results indicate that IDABC has promising advantages on PM/DPFSP.

关键词： Distributed scheduling Flowshop Preventive maintenance artificial bee colony

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ABC-TLBO: A Hybrid Algorithm Based on artificial bee colony and Teaching-Learning-Based Optimization

ABC-TLBO: A Hybrid Algorithm Based on Artificial Bee Colony ...

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第37届中国控制会议

作者： Mei Zhang Yuchong Pan Jinhui Zhu Guangsen Chen School of Automation Science and Engineering South China University of Technology School of Software Engineering South China University of Technology

artificial bee colony（ABC） is shown to be effective in solving optimization problems because of its good exploration capability. However it suffers from poor exploitation because onlooker bees use the same searching method as employed bees. In order to remedy the limited exploitation capability of ABC and achieve a better balance between exploration and exploitation, searching strategies for employed bees and onlooker bees are redesigned respectively and a novel algorithm called ABC-TLBO is proposed. It inherits the framework of basic ABC, and meanwhile an individualized TLBO based on teaching-learning-based optimization（TLBO） is embedded in onlooker bee phase to improve the exploitation capability of the presented algorithm. Moreover, a new searching strategy for employed bee phase which combines direction guiding searching and differential evolution crossover searching is applied to increase the population diversity and accelerate the convergence. The proposed ABC-TLBO algorithm is validated on benchmark functions and it is compared with ABC-based, TLBO-based and nature-inspired algorithms. Experimental results reveal that ABC-TLBO algorithm is an efficient, fast converging optimization method for solving global optimization problems.

关键词： artificial bee colony teaching-learning-based optimization global numerical optimization

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