Bees Algorithms Research Papers - Academia.edu (original) (raw)

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- Swarm Intelligence, Bees Algorithms, Honey bee, Search Algorithm
- by Mete Kalyoncu
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- Mobile Robotics, Optimal Control, Optimization techniques, Bees Algorithms

Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for
clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in
effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a
globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.

During the last 10 years there has been a considerable increase in the number of studies showing positive associations between spirituality and health. Incorporating spirituality into medical practice, however, continues to pose many challenges. These include the multicultural background in which medicine is practiced and the deeply personal meaning these issues carry for both patients and health care providers. A culturally sensitive spiritual assessment is a first step towards addressing the spiritual needs of patients. It also provides a tool through which health professionals can understand their own beliefs, prejudices; this includes: any thoughts or strong feelings about spirituality, religion, secular values and morales as they relate to those values, and needs related to health care. Terminology Words such as spirituality and religion carry a variety of meanings for different people. For some these terms evoke positive feelings and for others they may trigger negative responses. Although , by definition, the debate continues regarding the exact meaning of these words. I find it is helpful to have some shared meaning from which to start.

This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing
problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes
for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so
the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and
robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population
in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental
comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all
instances and is able to obtain 11 best results in comparison with the best-known results in
the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.

- by Sana Jawarneh and +1
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- Artificial Intelligence, Data Mining, Bees Algorithms, Vehicle Routing Problems
- by Mete Kalyoncu
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- Optimization techniques, Bees Algorithms, Control and Optimization, LQR Control

This paper describes the first application of the Bees Algorithm to mechanical design optimization. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the Bees Algorithm against other optimization techniques. The paper presents the results obtained showing the robust performance of the Bees Algorithm.

In this paper, we propose a novel efﬁcient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of proj...

- by Mohamed Amine Nemmich
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- Artificial Intelligence, Swarm Intelligence, Bees Algorithms, Optimization
- by Hans-Joerg Ferenz and +1
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- Bees Algorithms

In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing Bees Algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and effi...

- by Fatima Debbat and +1
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- Swarm Intelligence, Bees Algorithms, Optimization, Bioinspired Algorithms
- by Sameh Otri
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- Bees Algorithms
- by Mete Kalyoncu
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- Computer Science, Optimization techniques, Legged Robots, Bees Algorithms

A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm
mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a
kind of neighbourhood search combined with random search and can be used for both combinatorial
optimisation and functional optimisation. This paper focuses on the latter. Following a description of the
algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the
efficiency and robustness of the new algorithm.

- by Ahmed Haj Darwish and +2
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- Evolutionary algorithms, Bees Algorithms, Shape Grammars
- by Sameh Otri
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- Bees Algorithms, Clustering
- by Mehmet Emin Aydin
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- Engineering, Computer Science, Bees Algorithms, Artificial Neural Networks
- by Mete Kalyoncu
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- Engineering, Computer Science, Artificial Intelligence, Machine Learning

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods.
Keywords: Bees Algorithm (BA), Feature selection, Local search, Simple swap, 2-Opt and 3-Opt, 4-Opt approaches.

- by Aras Al-dawoodi
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- Optimization techniques, Bees Algorithms

In this paper, the Bees algorithm was used to train multi-layer perceptron neural networks to model the inverse kinematics of an articulated robot manipulator arm. The Bees Algorithm is a recently developed parameter optimisation algorithm that is inspired by the foraging behaviour of honey bees. The Bees Algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. Three neural networks were trained to reproduce a set of input/output numerical examples of the inverse kinematics of the main three joints of an articulated robotic manipulator. The results prove the remarkable robustness of the Bees Algorithm, which consistently trained the neural networks to model the kinematics data with very high accuracy. The learning results obtained by the proposed algorithm are compared to the results obtained by the standard Backpropagation Algorithm and an Evolutionary Algorithm. The comparative study highlights the superior performance of the proposed Bees Algorithm over the other algorithms.

- by Ali Fahmy
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- Robotics, Artificial Intelligence, Machine Learning, Evolutionary Computation

After web 2.0 technologies experienced a phenomenal expansion and high acceptance among private users, considerations are now intensified to assess whether they can be equally applicable, beneficially employed and meaningfully implemented in an entrepreneurial context. The fast-paced rise of social software like weblogs or wikis and the resulting new form of communication via the Internet is however observed ambiguously in the corporate environment. This is why the particular choice of the platform or technology to be implemented in this field is strongly dependent on its future business case and field of deployment and should therefore be carefully considered beforehand, as this paper strongly suggests.

- by Afshin Ghanbarzadeh
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- Bees Algorithms
- by Beste Koc
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- Swarm Intelligence, Bees Algorithms, Honey bee, Search Algorithm
- by Sameh Otri
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- Neural Networks, Bees Algorithms, Wood Defects
- by Salwani Abdullah
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- Computer Science, Algorithms, Artificial Intelligence, Data Mining
- by Afshin Ghanbarzadeh
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- Swarm Intelligence, Bees Algorithms, Honey bee, Search Algorithm

- by Fatima Debbat
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- Artificial Intelligence, Swarm Intelligence, Bees Algorithms, Optimization
- by Duc Pham
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- Artificial Intelligence, Machine Learning, Evolutionary Computation, Neural Networks

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud (DGC) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm (SBA) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

- by S. KAHAKI
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- Computer Science, Anomaly Detection, Bees Algorithms, Feature Selection

Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods oovercome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.

Polymeric materials, being capable of high mouldability, usability of long lifetime up to 50 years and availability at low cost properties compared to metallic materials, are in demand but finite element- based design engineers have limited means in terms of the limited material data and mathematical models. In particular, in the analysis of products with complex geometry, the stresses and strains of various amounts formed in the product should be known and evaluated in terms of a precise design of the product to fulfil life expectancy. Due to time and cost constraints, experimental data cannot be available for all cases required in analysis, therefore, finite element method-based simulations are commonly used by design engineers. This is also computationally expensive and requires a simpler and more precise way to complete the design more realistically. In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN models have provided much accurate and precise results, which are in line with 10% accuracy of experimental data.

- by Mehmet Emin Aydin and +1
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- Bees Algorithms, Artificial Neural Networks, Artificial Bee Colony Algorithm, Creep Behaviour Metals and Alloys