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Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

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NEURAL COMPUTING & APPLICATIONS 2019年第2-Sup期31卷 877-890页

作者： Kumar, Anil Kabra, Gaurav Mussada, Eswara Krishna Dash, Manoj Kumar Rana, Prashant Singh BML Munjal Univ Sch Management Gurgaon Haryana India Xavier Inst Management Dept Operat Management Bhubaneswar India BML Munjal Univ Sch Engn & Technol Gurgaon Haryana India Indian Inst Informat Technol & Management Gwalior Madhya Pradesh India Thapar Univ Patiala Comp Sci & Engn Dept Patiala Punjab India

Transactions through the web are now a progressive mechanism to access an ever-increasing range of services over more and more diverse environments. The internet provides many opportunities for companies to provide personalized online services to their customers, but the quality and novelty of some web services may adversely affect the appeal and user gratification. In the future, prediction of the consumer intention needs to be a main focus for selecting the web services for an application. The aim of this study is to predict online consumer repurchase intentions;to accomplish this objective a hybrid approach is chosen with a combination of machine learning techniques and artificial bee colony (ABC) algorithm being used. The study starts with identification of consumer characteristics for repurchase intention, followed by determining the feature selection of consumer characteristics and shopping mall attributes (with >0.1 threshold value) for the prediction model using ABC. Finally, validation using k-fold cross has been employed to measure the best classification model robustness. The classification models, viz. decision trees (C5.0), AdaBoost, random forest, support vector machine and neural network, are utilized to predict consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set shows that the AdaBoost method outperforms other classification models, with sensitivity and accuracy of 0.95 and 97.58%, respectively, on testing the data set. Examining the consumer repurchase intentions by considering both shopping mall and consumer characteristics makes this study unique.

关键词： artificial bee colony algorithm Classification Consumer k-Fold cross-validation Prediction Sensitivity

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A new improved artificial bee colony algorithm for ship hull form optimization

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ENGINEERING OPTIMIZATION 2016年第4期48卷 672-686页

作者： Huang, Fuxin Wang, Lijue Yang, Chi George Mason Univ Sch Phys Astron & Computat Sci Fairfax VA 22030 USA

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

关键词： artificial bee colony algorithm resistance ship hull form optimization

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An Efficient Routing Scheme for Intrabody Nanonetworks Using artificial bee colony algorithm

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IEEE ACCESS 2020年 8卷 98946-98957页

作者： Fahim, Hamza Javaid, Shumaila Li, Wei Ben Mabrouk, Ismail Al Hasan, Muath Rasheed, Muhammad Babar Xi An Jiao Tong Univ Sch Elect & Informat Xian 710049 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China COMSATS Univ Islamabad Dept Comp Sci Islamabad 45550 Pakistan Al Ain Univ Networks & Commun Engn Dept Abu Dhabi U Arab Emirates Univ Lahore Dept Elect & Elect Syst Lahore 54000 Pakistan

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and artificial colony algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

关键词： Nanoscale devices Data communication Routing Medical services Energy resources Energy consumption Routing protocols artificial bee colony algorithm nano router exponential weighted moving average intra-body nano-networks routing protocol

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Remanufacturing system scheduling of batch products with the consideration of dynamic changes in machine efficiency using an improved artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Jin, Qinyu Zhang, Shuai Xu, Jiyuan Jiang, Kang Cheng, Yuanzhe Zhang, Wenyu Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Sch Yingyang Financial Technol Hangzhou 310018 Peoples R China

Remanufacturing has the ability to bring the quality and condition of the recycled product back to the level of or even better than the new product. As remanufacturing continues to receive more and more attention from industry and academia, the scheduling of remanufacturing systems (RMS) has been extensively studied. However, few studies have not only focused on the dynamic changes (deterioration and learning) in machine efficiency during RMS scheduling but also considered that the three subsystems (disassembly, reprocessing and reassembly) of RMS collaborate with each other during scheduling. Therefore, this study proposes a new RMS scheduling model of batch recycled products with the consideration of dynamic changes in machine efficiency for handling the batch recycled products. It considers not only the collaboration among the three subsystems comprehensively, but also the dynamic changes in machine efficiency, i.e., deterioration effects and learning effects, during the scheduling. To solve this model, an improved artificial bee colony algorithm with a new representation scheme and a new population generating method is proposed. The effectiveness and practicality of this algorithm are examined through simulation experiments and comparison with other baseline algorithms.

关键词： Remanufacturing system RMS subsystems Scheduling Deterioration effects Learning effects artificial bee colony algorithm

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A multi-strategy fusion artificial bee colony algorithm with small population

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 142卷 112921-000页

作者： Song, Xiaoyu Zhao, Ming Xing, Shuangyun Shenyang Jianzhu Univ Informat & Control Engn Fac Shenyang 110168 Liaoning Peoples R China Shenyang Jianzhu Univ Sch Sci Shenyang 110168 Liaoning Peoples R China

Although artificial bee colony (ABC) algorithm is more and more popular in solving complex problems, slow convergence rate limits its wide application. ABC with small population can use the limited function evaluation times more efficiently since it can avoid unnecessary searches. However, ABC with small population cannot ensure population diversity, and when the algorithm is weak or unstable, it may fall into local optimum easily. So based on the latest research, we are motivated to propose a stabler and more efficient algorithm design to improve the search ability of ABC with small population by the fusion of multiple search strategies, which used together for the employed bees and the onlooker bees. Firstly we select and design multiple strategies with different search abilities of exploration and exploitation. Secondly, we propose an evolution ratio, which is an indicator to fully reflect the adaptability of the search strategy. Thirdly, we design different fusion methods according to the characteristics of the strategies, in which the search strategy with high exploration is maintained at a certain frequency throughout the whole search process of the employed bees, and the selections of the other two search strategies are adjusted according to evolution ratio adaptively in the employed bee phase and the onlooker bee phase. In the end, a novel algorithm called MFABC is proposed, which can realize efficiently multi-strategy cooperative search according to the requirements of different problems and different search stages. Experimental results on a set of benchmark functions have shown the accuracy, stability, efficiency and convergence rate of MFABC. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Optimization algorithm artificial bee colony algorithm Multi-strategy fusion Small population Cooperative searching

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An alternative artificial bee colony algorithm with destructive-constructive neighbourhood operator for the problem of composing medical crews

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INFORMATION SCIENCES 2016年 326卷 215-226页

作者： Delgado-Osuna, Jose A. Lozano, Manuel Garcia-Martinez, Carlos Andalusian Hlth Serv UH Reina Sofia Prov TIC Subdictorate Cordoba Cordoba 14004 Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Cordoba Comp & Numer Anal Dept E-14071 Cordoba Spain

We propose an artificial bee colony algorithm for the problem of composing medical crews with equity and efficiency principles. The objective is to constitute crews of practitioners with different skills and as efficient as possible. The artificial bee colony algorithm is a swarm intelligence model inspired in the foraging behaviour of honeybees. In this framework, bees produce candidate solutions for the problem by exploring the vicinity of food sources. The proposed approach exploits useful knowledge of the problem at this neighbourhood exploration, considering the partial destruction and heuristical reconstruction of selected solutions. We show the effectiveness of our model through an empirical analysis where three different contexts are considered: easy, challenging, and difficult problem cases. The results are compared with those of a genetic algorithm and the current state-of-the-art method for this problem. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Manpower planning Health service artificial bee colony algorithm Optimization

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Optimizing a stochastic disassembly line balancing problem with task failure via a hybrid variable neighborhood descent-artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第7期61卷 2307-2321页

作者： Guo, Hongfei Zhang, Linsheng Ren, Yaping Li, Yun Zhou, Zhongwei Wu, Jianzhao Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 509070 Peoples R China Jinan Univ Inst Phys Internet Zhuhai 509070 Peoples R China Jinan Univ Sch Management Guangzhou Peoples R China i4AI Ltd London England Tianjin Univ Sci & Technol Sch Elect Informat & Automat Tianjin Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China Natl Univ Singapore Dept Mech Engn Singapore Singapore

A disassembly line is an effective disassembly system to recover end-of-life products. In real life, as end-of-life products are subject to varying degrees of wear and tear, task failure may occur in the disassembly process. In this paper, the task failure risks are considered, and an expected profit-based stochastic disassembly line balancing problem is studied. First, a mathematical model is presented to maximise the expected recovering profit with task failures. Then, a hybrid metaheuristic approach is developed to efficiently solve the proposed model, which is integrated with a variable neighbourhood descent method and an artificial bee colony algorithm. Finally, the effectiveness and robustness of the proposed algorithm are verified by three cases, and experiment results show that the solution performance of the proposed approach is superior to the other three existing methods.

关键词： Disassembly line balancing problem task failure expected profit variable neighborhood descent method artificial bee colony algorithm

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Multiobjective optimization of torch brazing process by a hybrid of fuzzy logic and multiobjective artificial bee colony algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2016年第3期27卷 631-638页

作者： Alvarado-Iniesta, Alejandro Garcia-Alcaraz, Jorge L. Pina-Monarrez, Manuel Perez-Dominguez, Luis Autonomous Univ Ciudad Juarez Dept Ind & Mfg Engn Ave Charro 450 Norte Ciudad Juarez 32315 Chihuahua Mexico

This paper describes an application of a hybrid of fuzzy logic (FL) and multiobjective artificial bee colony algorithm (MOABC) for optimizing the torch brazing process of aluminum in the fabrication of condensers in the automotive manufacturing industry of Juarez, Mexico. This work aims to show how artificial intelligence is being applied in the manufacturing sector of Mexico for optimizing processes leading to cost reduction. The approach consists of using FL as surrogate model of the brazing process;after, MOABC is applied to find the nondominated solutions for leak rate which is a quality test of the condenser and production time. Results show the use of artificial intelligence is an excellent tool for optimizing manufacturing processes leading to improve productivity, mainly in the selected region, where this type of methodologies are fairly new in applicability.

关键词： Process optimization Fuzzy logic Multiobjective artificial bee colony algorithm Torch brazing Mexican industry

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artificial bee colony algorithm Optimized Support Vector Regression for System Reliability Analysis of Slopes

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2016年第3期30卷 04015040-04015040页

作者： Kang, Fei Li, Junjie Dalian Univ Technol Sch Hydraul Engn Fac Infrastruct Engn 2 Linggong Rd Dalian 116024 Peoples R China

Probabilistic stability analysis is an effective way to take uncertainties into account in evaluating the stability of slopes. This paper presents an intelligent response surface method for system probabilistic stability evaluation of soil slopes. artificial bee colony algorithm (ABC) optimized support vector regression (SVR) is used to establish the response surface to approximate the limit-state function. Then Monte Carlo simulation is performed via the ABC-SVR response surface to estimate system failure probability. The proposed methodology is verified in three case examples and is also compared with some well-known or recent algorithms for the problem. Results show that the new approach is promising in terms of accuracy and efficiency. (C) 2015 American Society of Civil Engineers.

关键词： Slope stability System reliability analysis Support vector machines artificial bee colony algorithm Particle swarm optimization Monte Carlo simulation

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A POWERFUL METHOD BASED ON artificial bee colony algorithm FOR TRANSLATIONAL MOTION COMPENSATION OF ISAR IMAGE

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2014年第11期56卷 2691-2698页

作者： Ustun, Deniz Ozdemir, Caner Akdagli, Ali Toktas, Abdurrahim Bicer, Mustafa Berkan Mersin Univ Dept Software Engn Fac Tarsus Technol TR-33400 Tarsus Mersin Turkey Mersin Univ Fac Engn Dept Elect Elect Engn TR-33343 Yenisehir Mersin Turkey Mersin Univ Dept Informat Technol TR-33343 Yenisehir Mersin Turkey

Inverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two-dimensional spatial distributions of radar cross-section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. (C) 2014 Wiley Periodicals, Inc.

关键词： ISAR imaging motion compensation minimum entropy method artificial bee colony algorithm

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