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2013 Asian Pacific Conference on Mechatronics and Control Engineering(APCMCE 2013)

作者： Najib Metni Jean Lahoud Notre Dame University

In this paper,the bee algorithm is used to train a Neural Network. This is done by altering the connections and biases of the Neural Network(NN) so that the desired output from the input is obtained. The merging between the two concepts is tested to control an inverted pendulum which is a benchmark for testing control theories. The trained NN is used to stabilize the pendulum in its upright position. The NN is trained by comparing its response to that of a state feedback controller. The bee algorithm succeeded in training the NN for it to have the desired output. Moreover,the effect of changing the parameters of both the neural network and the bee algorithm is also studied.

关键词： Neural Networks Artificial Intelligence bee algorithm Nonlinear Control

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A New Approach for Template Matching in Digital Images Using an Artificial bee Colony algorithm

A New Approach for Template Matching in Digital Images Using...

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World Congress on Nature and Biologically Inspired Computing

作者： Chidambaram, Chidambaram Lopes, Heitor Silverio Fed Technol Univ Parana UTFPR Bioinformat Lab Curitiba Parana Brazil

ISBN: (纸本)9781424450534

In this paper, we applied the Artificial bee Colony algorithm (ABC) to the object recognition in the images. ABC is a new metaheuristics approach inspired by the collective and individual foraging behavior of honey bee swarm. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene, considering that it may be translated, scaled, rotated and/or partially occluded. This will result in location of the given reference image in the target landscape image. Results of the experiments with grayscale and color images show that the ABC is faster than other evolutionary algorithms and with comparable accuracy.

关键词： bee algorithm Optimization Object Detection Images

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An online learning method for assessing smart grid stability under dynamic perturbations

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Alaerjan, Alaa Jabeur, Randa Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka Saudi Arabia

The increasing complexity of smart grid (SG) systems necessitates advanced methodologies to ensure their stability and reliability. In this work, we propose a novel online learning framework that leverages the bee algorithm for Ensemble Learning (BAEL) with dynamic perturbations to enhance the adaptability and performance of ML models in SG stability prediction. The key contributions of our approach are twofold. First, we introduce a dynamic perturbations mechanism that systematically adjusts variations within the bee algorithm, effectively balancing global exploration speed and local convergence accuracy throughout the learning process. Second, we integrate the BAEL strategy, where model selection and evolution are guided by performance-driven ensemble learning, allowing continuous adaptation to evolving data patterns. Through iterative learning cycles augmented by incremental perturbation adjustments, our method significantly improves predictive accuracy. To evaluate the effectiveness of our approach, we conduct extensive experimental assessments, demonstrating that our online learning process achieves an F1-score close to 100 percent. Additionally, we perform a comparative analysis between the bee algorithm and a benchmark fusion model incorporating Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGB) classifiers, under identical conditions, including the presence of dynamic perturbations. The results confirm that our BAEL-based approach consistently outperforms both the fusion of these classifiers and each of them operating independently across all evaluation metrics, highlighting its robustness in predicting SG stability under dynamic perturbations.

关键词： bee algorithm Dynamic perturbations Ensemble learning Fine-tuning Machine learning Online learning Smart grid

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On the performance of bee algorithms for resource-constrained project scheduling problem

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APPLIED SOFT COMPUTING 2011年第4期11卷 3720-3733页

作者： Ziarati, Koorush Akbari, Reza Zeighami, Vahid Shiraz Univ Dept Comp Sci & Engn Coll Engn Shiraz Iran Shiraz Univ Dept Math Shiraz Iran

This work investigates the application of bee algorithms for resource constrained project scheduling problem (or RCPSP). Three methods are developed based on recently introduced bee algorithms known as bee algorithm (BA), artificial bee colony (ABC), and bee swarm optimization (BSO). The proposed algorithms iteratively solve the RCPSP by utilizing intelligent behaviors of honey bees. Each algorithm has three main phases: initialization, update, and termination. At first phase, a set of schedules are generated randomly in order to initialize the population of the algorithms. Then, the initial population will be improved iteratively until termination condition is met. The update phase constitutes the body of each algorithm. Each algorithm uses different types of bees to provide appropriate level of exploration over search space while maintaining exploitation of good solutions. Three new local search methods are incorporated into the proposed methods in order to find more efficiency. Also, an efficient constraint handling method is introduced to resolve the infeasible solutions. The performances of the proposed algorithms are compared against a set of state-of-art algorithms. The simulation results showed that bee algorithms provides an efficient way for solving RCPSP and produce competitive results compared to other algorithms investigated in this work. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Artificial bee colony bee swarm optimization bee algorithm RCPSP

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Models and two-phase bee algorithms for multi-objective U-shaped disassembly line balancing problem

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OPTIMIZATION AND ENGINEERING 2023年第1期24卷 591-622页

作者： Li, Zixiang Kucukkoc, Ibrahim Tang, Qiuhua Zhang, Zikai Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Balikesir Univ Dept Ind Engn Cagis Campus TR-10145 Balikesir Turkey

Disassembly is the first and vital step in recycling and remanufacturing end-of-life products. Disassembly lines are utilized frequently due to high productivity and suitability. This research studies the disassembly line balancing problem on the U-shaped disassembly lines, which have higher flexibility than the traditional straight disassembly lines. A mixed-integer linear programming (MILP) model is developed to formulate the AND/OR precedence relationships with the objective of minimizing the number of stations. This model is also extended to a mixed-integer nonlinear programming model to optimize four objectives. To tackle this NP-hard problem effectively, a two-phase artificial bee colony algorithm and a bee algorithm are proposed and improved. In these algorithms, the first phase selects the stations with less loads on the last two stations for the purpose of achieving the optimal number of stations. The second phase hierarchically optimizes multiple objectives to achieve better line balances. Case studies show that the proposed MILP model obtains optimal solutions in terms of station number for the small-size instances, and the U-shaped disassembly lines obtain better fitness values than the straight disassembly lines. The comparative study demonstrates that the proposed methodologies perform competing performances in comparison with other 13 re-implemented algorithms, including tabu search algorithm, iterated local search algorithm, genetic algorithm, particle swarm optimization, three artificial bee colony algorithms and the original bee algorithm.

关键词： Disassembly line balancing U-Shaped disassembly line Artificial bee colony bee algorithm Integer programming Multi-objective optimization

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SEGMENTATION OF MEDICAL IMAGES BASED ON HIERARCHICAL EVOLUTIONARY AND bee algorithmS

SEGMENTATION OF MEDICAL IMAGES BASED ON HIERARCHICAL EVOLUTI...

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23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Azami, Hamed Azarbad, Milad Sanei, Saeid Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Babol Univ Technol Dept Elect & Comp Engn Babol Iran Univ Surrey Fac Engn & Phys Sci Guildford GU2 5XH Surrey England

ISBN: (纸本)9781479911806

Dynamic or adaptive thresholding strategy is of high interest in pattern recognition, signal and image processing. In this article a powerful method using a combination of multilevel thresholding algorithm, bee algorithm (BA), and hierarchical evolutionary algorithm (HEA) is proposed for segmentation of magnetic resonance images (MRIs). The HEA can be viewed as a modified variant of basic genetic algorithm (GA). The proposed method is based on the BA and, in fact, is an unsupervised clustering method depending on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not require to be known previously. The results show that the accuracy of the proposed algorithm is very excellent (about 97%).

关键词： Medical images hierarchical evolutionary algorithm multi-thresholding method bee algorithm

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Improved k-means clustering with Harmonic-bee algorithms 4

Improved k-means clustering with Harmonic-Bee algorithms

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4th World Congress on Information and Communication Technologies (WICT)

作者： Bonab, Mohammad Babrdel Hashim, Siti Z. Mohd Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia

ISBN: (纸本)9781479981144

Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the sensitivity to initial values and cluster centers or the risk of trapping in local optimal reduce its best performance. The purpose of kmeans method is minimizing the dissimilarity of observations, from cluster centers. In this paper, a new solution method inspired by harmony search combined with bee algorithm is introduced to improve performance k-means clustering. In this study, harmony and clustering structures are combined to produce harmony clustering. To avoid initial random selection, seed cluster center is considered in primary population as well as bee algorithm has been employed to increase the efficiency of algorithm. The proposed methods have been tested on standard benchmark data sets and also compared to other methods in the literature;it is noted that results show a promising performance leading to better efficiency and capability of the proposed solution.

关键词： harmony search algorithm bee algorithm k-means algorithm data clustering

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Lake Eutrophic Evaluation Based on bee Immune Evolutionary algorithm

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Agricultural Science & Technology 2010年第4期000卷 156-158,188页

作者： Yuan DANG Zuoyong LI Yanling ZOU 成都信息工程学院四川成都610225

In order to establish the lake eutrophic evaluation model for multiple indices, based on the gauge transformation, an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the "normalized values" of multi-indexes. The parameters in the formula were also optimized by bee immune evolutionary algorithm (BEIEA).The universal index formula was suitable to multi-indices items for eutrophic evaluation. At the same time, the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake. The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions. It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.

关键词： Lake Eutrophic evaluation bee algorithm bee immune evolutionary algorithm Parameter optimization

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Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human-robot collaboration

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APPLIED SOFT COMPUTING 2020年 93卷 106394-106394页

作者： Cil, Zeynel Abidin Li, Zixiang Mete, Suleyman Ozceylan, Eren Izmir Democracy Univ Dept Ind Engn Izmir Turkey Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Gaziantep Univ Dept Ind Engn Gaziantep Turkey

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mixed-model assembly line Human-robot collaboration bee algorithm Artificial bee colony algorithm Mixed-integer linear programming

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Construction of Cryptographically Strong S-Boxes Inspired by bee Waggle Dance

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NEW GENERATION COMPUTING 2016年第3期34卷 221-238页

作者： Isa, Herman Jamil, Norziana Z'Aba, Muhammad Reza Univ Tenaga Nas Coll Comp Sci & Informat & Technol Selangor Malaysia MIMOS Berhad Network Secur Lab Kuala Lumpur Malaysia

In this paper, we explore a heuristic method called the bee waggle dance to construct cryptographically strong S-boxes. The S-boxes exhibit good cryptographic properties such as high nonlinearity, low differential uniformity and high algebraic degree. The method involves the use of a trinomial power function as the initial S-box. The elements in the S-box are then permuted using the bee waggle dance algorithm. The S-boxes produced using this method are found to compare reasonably well with other existing S-boxes constructed using alternative heuristic methods. To the best of our knowledge, this is the first attempt to construct cryptographically strong S-boxes using the bee waggle dance algorithm.

关键词： Substitution Box S-Box Construction bee Waggle Dance bee algorithm Power Function

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