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9th International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2012)

作者： Choon, Yee Wen Mohamad, Mohd Saberi Deris, Safaai Chong, Chuii Khim Chai, Lian En Ibrahim, Zuwairie Omatu, Sigeru Univ Teknologi Malaysia Fac Comp Sci & Informat Syst Artificial Intelligence & Bioinformat Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Engn CAIRO Skudai 81310 Malaysia Osaka Inst Technol Informat & Commun Engn Dept Elect Osaka 5358585 Japan

ISBN: (纸本)9783642287640;9783642287657

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of bees algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E-coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems.

关键词： Evolutionary Programming Metabolic Engineering bees algorithm Gene Knockout Optimization

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Enhancing the speed of the bees algorithm using Pheromone-based Recruitment

Enhancing the speed of the Bees Algorithm using Pheromone-ba...

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7th IEEE International Conference on Industrial Informatics

作者： Packianather, M. S. Landy, M. Pham, D. T. Cardiff Univ Mfg Engn Ctr Queens Bldg Cardiff CF24 3AA S Glam Wales Univ Nantes Ecole Polytech F-44035 Nantes France

ISBN: (纸本)9781424437597

The bees algorithm is an intelligent optimisation tool mimicking the food foraging behaviour of honey bees. As a powerful search algorithm suitable for both continuous function and combinatorial optimisation it has gone through several modifications since its inception in order to improve its overall performance. This paper presents a new version of the bees algorithm which uses pheromone, a chemical substance secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species. The new bees algorithm employs the pheromone to attract bees to explore the promising regions of the search space. Following a description of the algorithm, the paper presents the results obtained for a number of benchmark problems for functional optimization. Compared to the original bees algorithm, the new version showed an average improvement of 41% in convergence speed.

关键词： bees algorithm Intelligent Optimisation Pheromone

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DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON bees algorithm

DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPEC...

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International Conference on Sensors and Models in Remote Sensing and Photogrammetry

作者： Samadzadegan, F. Hasani, H. Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran

ISBN: (纸本)9781629935201

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. bees algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

关键词： Hyperspectral LiDAR Fusion SVM Classifier Optimization Parameter Determination Feature Selection bees algorithm

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bees algorithm for null synthesizing of linear antenna arrays by controlling only the element positions

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NEURAL NETWORK WORLD 2007年第2期17卷 153-169页

作者： Guney, Kerim Onay, Murat Erciyes Univ Fac Engn Dept Elect & Elect Engn TR-38039 Kayseri Turkey Erciyes Univ Civil Aviat Sch Dept Aircraft Elect & Elect TR-38039 Kayseri Turkey

This paper presents bees algorithm (BA) for null steering of linear antenna arrays by controlling only the element positions. The BA is an optimization algorithm inspired by the natural foraging behavior of honey bees to find the optimal solution. To show the versatility and flexibility of the proposed BA, several examples of Chebyshev array pattern with the imposed single, multiple and broad nulls are given. It is found that the nulling technique based on BA is capable of steering the array nulls precisely to the undesired interference directions. For practical consideration, the sensitivity of the produced patterns due to small variations of the element positions is also examined by rounding the element position values to the second decimal position.

关键词： antenna array pattern nulling bees algorithm position-only control pattern synthesis

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A comparative study of the bees algorithm as a tool for function optimisation

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COGENT ENGINEERING 2015年第1期2卷 1-N.PAG页

作者： Pham, Duc Truong Castellani, Marco Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

The bees algorithm is a parameter optimisation method that mimics the foraging behaviour of honey bees. This paper presents an experimental study of the performance of the bees algorithm. Its strengths and weaknesses are analysed, and the most suitable parameterizations in relation to different optimisation tasks are revealed. The robustness of the optimisation results to reasonable modifications of the fitness landscape is studied for a large set of parameterizations. The bees algorithm is tested on 18 custom-made function minimisation benchmarks, and its performance compared to that of two state-of-the-art biologically inspired optimisation methods. Thanks to their two-dimensional nature, the proposed fitness landscapes are easy to visualise. Experimental evidence indicates also that they constitute a varied and challenging set of test cases, useful to reveal the specific abilities and biases of the search algorithms. In addition, the performance of the bees algorithm and the other two optimisation methods is tested on four real-world minimisation tasks from the literature. The results obtained on the benchmarks prove the effectiveness and robustness of the bees foraging metaphor, in particular on the most deceptive and high-dimensional fitness landscapes. They also confirm the ability of the bees algorithm to solve complex real-world optimisation tasks.

关键词： optimisation bees algorithm swarm intelligence evolutionary algorithms optimisation benchmarks variable neighbourhood search

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A Binary bees algorithm for P300-Based Brain-Computer Interfaces Channel Selection 16th

A Binary Bees Algorithm for P300-Based Brain-Computer Interf...

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14th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Martinez-Cagigal, Victor Hornero, Roberto Univ Valladolid Biomed Engn Grp ETSI Telecomunicac Valladolid Spain Univ Valladolid Inst Invest Matemat IMUVA Valladolid Spain Inst Neurociencias Castilla & Leon INCYL Salamanca Spain

ISBN: (纸本)9783319591476;9783319591469

Brain-computer interface (BCI) systems need to work in real-time with large amounts of data, which makes the channel selection procedures essential to reduce over-fitting and to increase users' comfort. In that sense, metaheuristics based on swarm intelligence (SI) have demonstrated excellent performances solving complex optimization problems and, to the best of our knowledge, they have not been fully exploited in P300-BCI systems. In this study, we propose a modified SI method, called binary bees algorithm (b-BA), that allows users to select the most relevant channels in an evolutionary way. This method has been compared to particle swarm optimization (PSO) and tested with the 'III BCI Competition 2005' dataset II. Results show that b-BA is suitable for use in this kind of systems, reaching higher accuracies (mean of 96.0 +/- 0.0%) than PSO (mean of 93.5 +/- 2.1%) and the original ones (mean of 94.0 +/- 2.8%) using less than the half of the initial channels.

关键词： Brain-computer interfaces P300 evoked potentials Channel selection Swarm intelligence bees algorithm Particle swarm optimization

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Minimum-Time Motion Planning for a Robot Arm Using the bees algorithm

Minimum-Time Motion Planning for a Robot Arm Using the Bees ...

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7th IEEE International Conference on Industrial Informatics

作者： Ang, M. C. Pham, D. T. Ng, K. W. Cardiff Univ Mfg Engn Ctr Cardiff CF24 3AA Wales

ISBN: (纸本)9781424437597

The problem of determining the minimum motion time for a robot arm is not new. Several approaches have been successfully proposed to solve this problem. One of these approaches involves generating a fixed number of joint displacements to construct the joint trajectories via cubic spline functions before scaling the travel time to avoid violating kinematic constraints such as the maximum permissible velocity, acceleration and jerk. Conforming to these kinematic constraints is necessary to prevent trajectories that are undulant and normally unsuitable for robotic motion. This paper presents a Pareto-based multi-objective bees algorithm to determine the minimum travelling time for a SCARA-type robot arm that takes consideration of trajectory smoothness. The results obtained are better than those reported for solutions using the genetic algorithm (with breeder genetic algorithm operators and the path redistribution with relaxation operator), the Nelder-Mead flexible polyhedron search and the improved polytope algorithm with a penalty function.

关键词： robot motion planning multi-objective optimisation bees algorithm

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The bees algorithm and mechanical design optimisation

The Bees Algorithm and mechanical design optimisation

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5th International Conference on Informatics in Control, Automation and Robotics

作者： Pham, D. T. Castellani, M. Sholedolu, M. Ghanbarzadeh, A. Cardiff Univ Mfg Engn Ctr Cardiff Wales

ISBN: (纸本)9789898111302

The bees algorithm is a search procedure inspired by the way honey-bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the bees algorithm against other optimisation techniques. The paper presents the results obtained showing the robust performance of the bees algorithm.

关键词： bees algorithm optimisation mechanical design

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A Discrete Hybrid bees algorithm for Service Aggregation Optimal Selection in Cloud Manufacturing

A Discrete Hybrid Bees Algorithm for Service Aggregation Opt...

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Tian, Sisi Liu, Quan Xu, Wenjun Yan, Junwei Minist Educ Key Lab Fiber Opt Sensing Technol & Informat Proc Wuhan 430070 Peoples R China

ISBN: (纸本)9783642412783;9783642412776

Facing to the globalization and increasing competition of manufacturing enterprise, how to integrate the existent manufacturing services in cloud manufacturing model to form the newly value-added services in order to fulfill the user requirements has become a significant issue in manufacturing area. In this context, a discrete hybrid bees algorithm (DHBA) is proposed to solve service optimal the selection in resource service aggregation. The problem of service aggregation with QoS global optimal is transformed into a multi-objective services aggregation optimization with QoS constraints, and DHBA is utilized to produce a near-optimal solution. A case study together with a set of simulation experiment is presented and the results demonstrate the effectiveness and feasibility of the proposed method.

关键词： Cloud manufacturing service aggregation bees algorithm Pareto optimal

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Optimal Placement of the Interline Power Flow Controller using the bees algorithm to minimize power loss 4

Optimal Placement of the Interline Power Flow Controller usi...

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4th International Conference on Electrical Energy Systems (ICEES)

作者： Rajagopalan, Srinivasan Thippana, Vishnu Charan Parimi, Alivelu Manga BITS Pilani Dept EEE Hyderabad Telangana India

ISBN: (纸本)9781538636954

The Interline Power Flow Controller (IPFC) is a series FACTS (Flexible AC Transmission Systems) that has been used fur various applications such as power flow control, automatic generation control, oscillation damping, congestion management, power system state estimation and power system protection. The effectiveness of the IPFC varies with position in the power system and so there is motivation to determine the optimal location of the device. Additionally, the optimal location for one application need not necessarily be the optimal location for other applications as well. This study optimally locates the device in order to minimize the power losses in the power system using the bees algorithm. Simulations were run on a 5 bus system in MATLAB. Finally, the results obtained from using the bees algorithm were compared with results obtained using Particle Swarm Optimization.

关键词： FACTS Devices Interline Power Flow Controller (IPFC) power losses bees algorithm Particle Swarm Optimization

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