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An Effective Hybrid of bees algorithm and Differential Evolution algorithm in Data Clustering

MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-17页

作者： Bonab, Mohammad Babrdel Hashim, Siti Zaiton Mohd Bazin, Nor Erne Nazira Alsaedi, Ahmed Khalaf Zager Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Misan Univ Coll Sci Minist Higher Educ Iraq Maysan Iraq

Clustering is one of the most commonly used approaches in data mining and data analysis. One clustering technique in clustering that gains big attention in clustering related research is k-means clustering such that the observation is grouped into k cluster. However, some obstacles such as the adherence of results to the initial cluster centers or the risk of getting trapped into local optimality hinder the overall clustering performance. The purpose of this research is to minimize the dissimilarity of all points of a cluster from gravity center of the cluster with respect to capacity constraints in each cluster, such that each element is allocated to only one cluster. This paper proposes an effective combination algorithm to find optimal cluster center for the analysis of data in data mining and a new combination algorithm is proposed to untangle the clustering problem. This paper presents a new hybrid algorithm, which is, based on cluster center initialization algorithm (CCIA), bees algorithm (BA), and differential evolution (DE), known as CCIA-BADE-K, aiming at finding the best cluster center. The proposed algorithm performance is evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in the literature confirm its superior performance and higher efficiency.

关键词： bees algorithm DIFFERENTIAL evolution (Computer science) DATA mining K-means clustering OBSERVATION (Scientific method)

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A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains

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BIOPROCESS AND BIOSYSTEMS ENGINEERING 2014年第3期37卷 521-532页

作者： Choon, Yee Wen Mohamad, Mohd Saberi Deris, Safaai Illias, Rosli Md Chong, Chuii Khim Chai, Lian En Univ Teknol Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Dept Bioproc Engn Fac Chem Engn Skudai 81310 Johor Malaysia

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of bees algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy.

关键词： bees algorithm Flux balance analysis Optimization Microbial strains Metabolic engineering

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Evaluation and comparison of Genetic algorithm and bees algorithm for location-allocation of earthquake relief centers

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2016年 15卷 94-107页

作者： Saeidian, Bahram Mesgari, Mohammad Saadi Ghodousi, Mostafa KN Toosi Univ Technol Fac Geodesy & Geomat GIS Div Tehran Iran KN Toosi Univ Technol Geoinformat Technol Ctr Excellence Tehran Iran

One of the important steps in the earthquake disaster management is the establishment of temporary relief centers, to provide the basic helps and support in short time. Finding optimum location for these centers with adequate covering of the urban areas is not a trivial problem. The meta-heuristic algorithms are promising methods, capable of solving such complex optimization problems. The goal of this research was to compare the performance of Genetic algorithm (GA) and bees algorithm (BA) in finding the optimum location of relief centers and in allocating of the parcels to them. In order to limit the search space, GIS was used for selecting a few candidate sites that satisfy the initial conditions and criteria. Then, the two algorithms were used to select nine optimum sites among the candidates and to allocate the parcels to them, while minimizing the sum of all distances between centers and parcels. To calibrate the parameters of the algorithms, a simple simulated data set was used. Having proper values for those parameters, the algorithms were tested on the real data of the study area. The results showed that the convergence of the BA was rather gradual, while the trend for GA was relatively stepwise. Both algorithms showed high levels of repeatability. For both the simulated and real data, GA showed to be faster than BA. Simplicity and repeatability of the algorithm are the main factors from the user's point of view. Therefore, considering these criteria, the GA is more favorable than the BA. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Relief center Disaster Genetic algorithm bees algorithm GIS Location-allocation

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SYNTHESIS OF THINNED LINEAR ANTENNA ARRAYS USING bees algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2011年第4期53卷 795-799页

作者： Guney, K. Onay, M. Nuh Naci Yazgan Univ Fac Engn Kayseri Turkey Erciyes Univ Civil Aviat Sch Dept Aircraft Elect & Elect TR-38039 Kayseri Turkey

In this article, we propose a method based on the bees algorithm (BA) to synthesize thinned linear antenna arrays with low sidelobe level. The BA is an optimization algorithm inspired by the behavior of the honey bees to find the optimal way of harvesting food resources around the hive. Several examples with different thinning percentages are given to show the effectiveness of the proposed method. The results of BA are compared with the results of real coded genetic algorithm, ant colony optimization, and iterative Fourier technique. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:795-799, 2011;View this article online at ***. DOI 10.1002/mop.25850

关键词： bees algorithm thinned array array synthesis sidelobe control

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A fast scheme for multilevel thresholding based on a modified bees algorithm

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KNOWLEDGE-BASED SYSTEMS 2016年 101卷 114-134页

作者： Hussein, Wasim A. Sahran, Shahnorbanun Abdullah, Siti Norul Huda Sheikh Univ Kebangsaan Malaysia Fac Informat Syst & Technol Ctr Artificial Intelligence Technol Pattern Recognit Res Grp Bandar Baru Bangi 43650 Malaysia

Image segmentation is one of the most important tasks in image processing and pattern recognition. One of the most efficient and popular techniques for image segmentation is image thresholding. Among several thresholding methods, Kapur's (maximum entropy (ME)) and Otsu's methods have been widely adopted for their simplicity and effectiveness. Although efficient in the case of bi-level thresholding, they are very computationally expensive when extended to multilevel thresholding because they employ an exhaustive search for the optimal thresholds. In this paper, a fast scheme based on a modified bees algorithm (BA) called the Patch-Levy-based bees algorithm (PLBA) is adopted to render Kapur's (ME) and Otsu's methods more practical;this is achieved by accelerating the search for the optimal thresholds in multilevel thresholding. The experimental results demonstrate that the proposed PLBA-based thresholding algorithms are able to converge to the optimal multiple thresholds much faster than their corresponding methods based on Basic BA. The experiments also show that the thresholding algorithms based on BA algorithms outperform corresponding state-of-the-art metaheuristic-based methods that employ Bacterial Foraging Optimization (BFO) and quantum mechanism (quantum-inspired algorithms) and perform better than the non-metaheuristic-based Two-Stage Multi-threshold Otsu method (TSMO) in terms of the segmented image quality. In addition, the results show the high degree of stability of the proposed PLBAbased algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multilevel thresholding Otsu thresholding Maximum entropy thresholding bees algorithm Patch environment Levy flight

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Patch-Levy-based initialization algorithm for bees algorithm

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APPLIED SOFT COMPUTING 2014年 23卷 104-121页

The bees algorithm (BA) is a population-based metaheuristic algorithm inspired by the foraging behavior of honeybees. This algorithm has been successfully used as an optimization tool in combinatorial and functional optimization fields. In addition, its behavior very closely mimics the actual behavior that occurs in nature, and it is very simple and easy to implement. However, its convergence speed to the optimal solution still needs further improvement and it also needs a mechanism to obviate getting trapped in local optima. In this paper, a novel initialization algorithm based on the patch concept and Levy flight distribution is proposed to initialize the population of bees in BA. Consequently, we incorporate this initialization procedure into a proposed enhanced BA variant. The proposed variant is more natural than conventional variants of BA. It mimics the patch environment in nature and Levy flight, which is believed to characterize the foraging patterns of bees in nature. The results of experiments conducted on several widely used high-dimensional benchmarks indicate that our proposed enhanced BA variant significantly outperforms other BA variants and state-of-the-art variants of the Artificial Bee Colony (ABC) algorithm in terms of solution quality, convergence speed, and success rate. In addition, the results of experimental analyses conducted indicate that our proposed enhanced BA is very stable, has the ability to deal with differences in search ranges, and rapidly converges without getting stuck in local optima. (C) 2014 Elsevier B.V. All rights reserved.

关键词： bees algorithm Metaheuristic algorithm Population initialization Levy flight Patch environment

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Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled bees algorithm

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BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2018年第3期100卷 402-408页

作者： Farajvand, Mohammad Kiarostami, Vahid Davallo, Mehran Ghaedi, Abdolmohammad Islamic Azad Univ Dept Chem North Tehran Branch Tehran Iran Islamic Azad Univ Gachsaran Branch Dept Chem Gachsaran Iran

A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu2+ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22-140, 0.08 and 0.22 A mu g L-1, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L-1 were in the range of 85-98%.

关键词： Solvent terminated dispersive liquid-liquid microextraction Response surface methodology Copper ions bees algorithm

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bees algorithm with Integration of Probabilistic Models for Global Optimization 3rd

Bees Algorithm with Integration of Probabilistic Models for ...

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3rd Symposium on Intelligent Manufacturing and Mechatronics, SympoSIMM 2020

作者： Bahari, Muhammad Syahril Azmi, Nur Athirah Yusof, Zahayu Md Pham, Duc Truong School of Manufacturing Engineering Universiti Malaysia Perlis Arau Perlis Malaysia School of Quantitative Sciences UUM College of Arts and Sciences Universiti Utara Malaysia Sintok Kedah06010 Malaysia School of Mechanical Engineering College of Physical Sciences University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9789811608650

The standard bees algorithms (SBA) is a population-based search algorithm inspired by the nature that revolves around mimicking the food foraging behavior of honey bees in order to solve optimization problems. This study had implemented the probabilistic method in Estimated Distribution algorithm (EDA) into the SBA to improve the performance of the algorithm in terms of speed and accuracy. The newly proposed algorithm is tested on ten benchmark test functions. Then, the accuracy and speed are compared to SBA. The performance of the algorithm had also been validated on two engineering design optimization problems with specific constraints condition. The results of the benchmark test functions showed that the proposed algorithm provides very competitive results in terms of improved speed and convergence rate. The results of the design engineering optimization problems prove that the proposed algorithm can perform well in solving challenging problems with constrained and unknown search spaces. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： bees algorithm Optimization algorithm Probabilistic model

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Dynamic optimisation by a modified bees algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2012年第I7期226卷 956-971页

作者： Castellani, Marco Pham, Q. Tuan Pham, Duc T. Univ Bergen Dept Biol Theoret Ecol Grp N-5020 Bergen Norway Univ New S Wales Sch Chem Engn Sydney NSW 2052 Australia Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

A modified bees algorithm was applied to dynamic optimisation problems in chemical engineering. A two-level factorial experiment was used to tune the settings of the population parameters, based on the premise that it is most important to avoid those configurations that cause the worst performances than to look for those that reach the best performance. Tested on eight well known benchmark problems, the tuned algorithm outperformed the standard bees algorithm and other two well known optimisation methods. The performance of the proposed algorithm was also competitive with that of the state-of-the-art in the literature, and the solutions produced were very close to the known optima of the benchmarks. The results demonstrate the efficacy of the modified bees algorithm as a tool for the solution of dynamic optimisation problems. The results also proved the effectiveness of the proposed statistical parameter tuning algorithm, and indicated its competitiveness as an alternative to the standard complex and subjective trial-and-error methods.

关键词： Evolutionary optimisation tuning dynamic optimisation factorial experiment parameter setting swarm intelligence bees algorithm

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Artificial Neural Network and bees algorithm for Removal of Eosin B Using Cobalt Oxide Nanoparticle-Activated Carbon: Isotherm and Kinetics Study

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2015年第1期34卷 155-168页

作者： Ghaedi, Mehrorangm Ansari, Amin Nejad, Payman Assefi Ghaedi, Abdolmohammad Vafaei, Azam Habibi, Mohammad Hossein Univ Yasuj Dept Chem Yasuj 7591435 Iran Islamic Azad Univ Dept Chem Sci & Res Branch Fars Iran Islamic Azad Univ Dept Chem Gachsaran Branch Gachsaran Iran Univ Isfahan Dept Chem Nanotechnol Lab Esfahan Iran

The objective of this work is the study of adsorption of Eosin B by cobalt oxide nanoparticle loaded on activated carbon (Co2O3-NP-AC). This new material with high efficiency in a routine manner was synthesized in our laboratory, and its surface properties such as surface area, pore volume, and functional groups were characterized with different techniques such X-ray diffraction, Brunauer, Emmett, and Teller, and scanning electron microscopy analysis. The effect of solution pH, adsorbent dosage (0.005-0.02 g), contact time (0.5-30 min), and initial concentration of dye (30-80 mg L-1) on the adsorption process was investigated. Thus, Langmuir, Freundlich, Tempkin, and D-R isothermal models are applied for fitting the experimental data, and the data well presented by Langmuir model with a maximum adsorption capacity of 588.2 mg g(-1) at 25 degrees C. Kinetic studies at various adsorbent dosage and initial Eosin B concentration show that maximum Eosin B removal was achieved within 18 min of the start of every experiment at most conditions. The combination of pseudo-second-order rate equation and intraparticle diffusion model (with removal more than 99%) is usable to explain the experimental data of adsorption process at all conditions. The influences of parameters including initial dye concentration, adsorbent dosage (g), and contact time on Eosin B adsorption onto cobalt oxide nanoparticles loaded on AC were investigated by multiple linear regression (MLR) and artificial neural network (ANN), and the influences of variables were optimized using bees algorithm. Comparison of the results obtained using introduced models showed the ANN model is better than the MLR model for prediction of Eosin B removal using cobalt oxide nanoparticles loaded on AC. Using the optimal ANN model, the coefficients of determination (R-2) were 0.9965 and 0.9936;mean squared error values were 0.00015 and 0.00029 for training and testing data, respectively. (c) 2014 American Institute of Chemic

关键词： Cobalt Oxide Nanoparticles Eosin B Artificial Neural Network bees algorithm Kinetics Isotherm

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