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Patch-Levy-based initialization algorithm for bees algorithm

APPLIED SOFT COMPUTING 2014年 23卷 104-121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

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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Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

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INFORMATION SCIENCES 2014年 258卷 80-93页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei Taiwan Natl Taiwan Univ Sci & Technol Ecol & Hazard Mitigat Engn Researching Ctr Taipei Taiwan

Swarm intelligence (SI) has generated growing interest in recent decades as an algorithm replicating biological and other natural systems. Several SI algorithms have been developed that replicate the behavior of honeybees. This study integrates two of these, the artificial bee colony (ABC) and bees algorithms (BA), into a hybrid ABC-BA algorithm. In ABC-BA, an agent can perform as an ABC agent in the ABC sub-swarm and/or a BA agent in the BA sub-swarm. Therefore, the ABC and BA formulations coexist within ABC-BA. Moreover, the population sizes of the ABC and BA sub-swarms vary stochastically based on the current best fitness values obtained by the sub-swarms. This paper conducts experiments on six constrained optimization problems (COPs) with equality or inequality constraints. In addressing equality constraints, this paper proposes using these constraints to determine function variables rather than directly converting them into inequality constraints, an approach that perfectly satisfies the equality constraints. Experimental results demonstrate that the performance of the ABC-BA approximates or exceeds the winner of either ABC or BA. Therefore, the ABC-BA is recommended as an alternative to ABC and BA for handling COPs. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Constrained optimization problem Swarm intelligence Artificial bee colony bees algorithm

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Thermodynamic and Economic Optimization of Plate Fin Heat Exchangers Using the bees algorithm

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HEAT TRANSFER-ASIAN RESEARCH 2014年第5期43卷 427-446页

作者： Banooni, Salem Zarea, Hossein Molana, Maysam Shahid Chamran Univ Engn Fac Dept Mech Engn Ahwaz Iran

This study presents the successful application of the bees algorithm (BA) for optimal design of a cross- flow plate fin heat exchanger by offset strip fins. The epsilon - NTU method is used to approximate the heat exchanger effectiveness and pressure drop. Two different objective functions including the minimization of total annual cost (sum of investment and operational costs) and total number of entropy generation units for certain heat duty required under given space constraints are considered as targets of optimization separately. Based on the applications, seven design parameters (heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length, and number of hot side layers) are selected as optimization variables. Two examples from the literature are presented to illustrate the efficiency and accuracy of the proposed algorithm. Results showed that the BA can detect an optimum configuration with higher speed (short computational time) and accuracy compared to the imperialist competitive algorithm (ICA) and the genetic algorithm (GA). (C) 2013 Wiley Periodicals, Inc.

关键词： plate fin heat exchanger optimization bees algorithm imperialist competitive algorithm genetic algorithm

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Optimization of composite laminates with free edges under extension load and thermal load

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ADVANCED COMPOSITES AND HYBRID MATERIALS 2022年第3期5卷 2206-2222页

作者： Kazemshahi, Behnam Poor, Mohammad Mansuri Saz, Mohammad Shishe Islamic Azad Univ Dept Mech Engn Parand Branch Parand Iran Univ Tehran Dept Mech Engn Tehran Iran Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

In this study, the single-objective and multi-objective optimizations of composite laminates with free-edge boundaries are presented. bees algorithm (BA) is used to search for the optimal design. An analytical technique is utilized to calculate the edge effect stresses at the free boundaries of the laminates. For this purpose, two laminates are considered: a flawless laminate with free straight edges and a laminate with a hole. The two laminates are subjected to uni-axial and thermal loads. The design of optimal layup in single-objective optimization is based on maximizing the interlaminar strength of the laminates. The multi-objective optimization is formulated for maximizing the interlaminar strength, minimizing the weight and the total cost of the composite components. The optimization variables are considered to be the number of layers, stacking sequence, and the thickness of each layer. Moreover, to verify the performance of BA, the results are compared with those of particle swarm optimization (PSO) and those based on resulting from multi-objective optimization of Kursave (KUR) function. The results of this study show the effect of geometric conditions of free edges as well as the type of loading (mechanical and thermal) on the optimization of composite laminates.

关键词： bees algorithm Composite laminate Interlaminar stress Single-objective optimization Multi-objective optimization

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Design Optimisation of Permanent Magnet Synchronous Motor for Electric Vehicles Traction using the bees algorithm 49

Design Optimisation of Permanent Magnet Synchronous Motor fo...

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49th International Universities Power Engineering Conference (UPEC)

作者： Braiwish, N. Y. Anayi, F. J. Fahmy, A. A. Eldukhri, E. E. Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF10 3AX S Glam Wales

ISBN: (纸本)9781479965571

Electric machines designs for traction application are concerned with paying particular attention to power density and efficiency. Therefore, this paper is focused on applying bees algorithm (BA) for optimal design of Brushless Permanent Magnet Synchronous Motor (BLPMSM) for propulsion application. The analytical approach for the motor magnetic circuit is performed using the radial instantaneous magnetic field distribution in the airgap under specified loading condition;taking into account the magnetic core saturation and motor overall performance. The design aims to maximize the power density. Therefore, the optimisation objective function is formed to minimise motor weight and maximise efficiency. While the bees algorithm (BA) is applied to search for the optimum design parameters;the optimised design is then verified using Finite Element Method (FEM). Comparing with an existing machine, called here basic motor;the obtained results show that motor weight can be reduced by approximately 20%, while motor output power is kept constant. As the motor is designed for traction applications, the characteristics of the developed torque and speed were investigated under different gearing levels using magnetic gearing technique, the efficiency shows improvements in comparison to the basic motor.

关键词： bees algorithm Brushless Permanent Magnet Synchronous Motor BLPMSM Finite Element Method FEM Magnetic Gearing Multi-objective function

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Cloud remanufacturing: Remanufacturing enhanced through cloud technologies

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 64卷 133-148页

作者： Caterino, Mario Fera, Marcello Macchiaroli, Roberto Pham, Duc Truong Univ Campania Luigi Vanvitelli Dept Engn Aversa Italy Univ Birmingham Dept Mech Engn Birmingham England

Remanufacturing represents one of the most promising strategies for promoting circular economy (CE) principles and achieving economic and environmental sustainability goals. The implementation of Industry 4.0 (I4.0) enabling technologies such as cloud computing and the Internet of Things in the classical remanufacturing process provides the opportunity to define a new approach called Cloud Remanufacturing, which realises a distributed system in which remanufacturing resources, located at different geographic sites, are shared and managed in a centralised environment. This paper aims to introduce the new concept of Cloud Remanufacturing by proposing a framework to explain its logic and characterise its main features. Furthermore, the mathematical relationships for evaluating the costs and the times for completing the remanufacturing process enhanced by I4.0 technologies will be developed, and, relying on them, the model will be tested for 2 scenarios involving different extended areas. In detail, the problem of activity scheduling in Cloud Remanufacturing will be addressed using two metaheuristic approaches. The results show the effectiveness and theoretical applicability of the model, even if a comparison with a real case study is not possible due to the lack of a real centrally managed remanufacturing distributed system.

关键词： Remanufacturing Circular economy Cloud technologies Makespan problem Tabu Search bees algorithm

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Application of Nature Inspired algorithms to Multi-objective Optimization of New Generation Network Problem 22

Application of Nature Inspired Algorithms to Multi-objective...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Kozdrowski, Stanislaw Wnuk, Kacper Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9781450392686

The subject of the paper is the application of metaheuristic algorithms inspired by nature for multi-criteria optimization in new generation optical networks. In the considered optical network, criteria related to the structure and topology of the network and the equipment used were taken into account. Network criteria include the length of optical channels, optical fiber attenuation, and dispersion. On the other hand, the hardware criteria include the cost of transponders and a finite range of frequency slides in the optical spectrum of the optical fiber. Several nature-inspired metaheuristics were used for the multi-criteria optical network optimization problem. The proposed algorithm, based on the bee algorithm was compared with others taken from the literature. Simulation results of all algorithms were implemented and carried out using test networks with topology typical for telecommunication networks. The proposed and improved algorithm obtained good results that encourage further work and research.

关键词： multi-objective optimization metaheuristics evolutionary algorithm bees algorithm combinatorial optimization optical network design

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Deep Learning: Parameter Optimization Using Proposed Novel Hybrid bees Bayesian Convolutional Neural Network

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APPLIED ARTIFICIAL INTELLIGENCE 2022年第1期36卷 Article: 2031815页

作者： Alamri, Nawaf Mohammad H. Packianather, Michael Bigot, Samuel King Abdulaziz Univ Jeddah Saudi Arabia Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the bees algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms.

关键词： Convolutional neural network bees algorithm Hybrid algorithm Deep learning Neural network Bayesian optimization

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Extending cutting tool remaining life through deep learning and laser shock peening remanufacturing techniques

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JOURNAL OF CLEANER PRODUCTION 2024年 477卷

作者： Liang, Yuchen Wang, Yuqi Lu, Jinzhong Jiangsu Univ Sch Mech Engn Zhenjiang Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Peoples R China

Predicting and extending the remaining life of cutting tools during machining processes is crucial for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to diverse working conditions throughout the machining process lifecycle. This paper introduced a comprehensive framework that effectively addressed the challenges by integrating multi-source data and using deep learning techniques. The system integrated power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines with the following innovations: (1) A standardized data fusion method was developed to integrate multi-source data sources, the hybrid graph convolutional network (GCN) with attention mechanisms was employed to improve the prognosis accuracy of cutting tool remaining life, best accuracy of 98.56% and average accuracy of 97.71% were achieved. (2) The optimization of laser shock peening (LSP) remanufacturing parameters using the bees algorithm showed good performance, a fitness value of 0.95 was achieved with convergence within 15 iterations. (3) Monitoring of the LSP remanufacturing process was designed based on sound and vibration data for optimal remanufacturing performance. (4) The remanufacturing approach in extending the remaining life of cutting tool was validated through FEA analysis and experimental testing, cutting tool life was extended by 29.32% to achieve a sustainable manufacturing process.

关键词： Cutting tool prognosis Data fusion Graph convolutional network (GCN) Laser shock peening (LSP) remanufacturing bees algorithm

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Monitoring method for gasification process instability using BEE-RBFNN pattern recognition

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第29期46卷 16202-16216页

作者： Zhang, Jinchun Zhang, Zichuan Hou, Jinxiu Henan Polytech Univ Sch Energy Sci & Engn Jiaozuo 454000 Henan Peoples R China Henan Polytech Univ Sch Safety Sci & Engn State Key Lab Cultivat Base Gas Geol & Gas Contro Jiaozuo 454000 Henan Peoples R China

To identify large fluctuations of parameters in the process of gasification, a control chart pattern recognition method based on optimized radial basis function neural network (RBFNN) is proposed in this paper to conduct pattern recognition of the parameters of gasification process. The proposed method is described in four aspects, feature description, feature extraction, classifier and algorithm learning and training. The initial data is described from shape features and statistical features to reduce the dimension of the data. The optimal characteristic set is selected by the association rule mining algorithm to reduce the complexity of the model. The performance of neural network is affected by the learning method. With RBFNN as a classifier, a learning method based on bees algorithm is therefore put forward and then training recognition is compared between the proposed method and the traditional method. The results show that the proposed method has higher recognition rate and simpler structure than the traditional method, and it is very effective in monitoring and identifying abnormal fluctuations of gasification process parameters. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Gasification process Instability monitoring Pattern recognition Radial basis neural network bees algorithm

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