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Feature selection based on artificial bee colony and gradient boosting decision tree

APPLIED SOFT COMPUTING 2019年 74卷 634-642页

作者： Rao, Haidi Shi, Xianzhang Rodrigue, Ahoussou Kouassi Feng, Juanjuan Xia, Yingchun Elhoseny, Mohamed Yuan, Xiaohui Gu, Lichuan Anhui Agr Univ Coll Comp & Informat Hefei 230036 Anhui Peoples R China Minist Agr Key Lab Agr Elect Commerce Hefei 230036 Anhui Peoples R China Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA Mansoura Univ Mansoura 35516 Egypt

Data from many real-world applications can be high dimensional and features of such data are usually highly redundant. Identifying informative features has become an important step for data mining to not only circumvent the curse of dimensionality but to reduce the amount of data for processing. In this paper, we propose a novel feature selection method based on bee colony and gradient boosting decision tree aiming at addressing problems such as efficiency and informative quality of the selected features. Our method achieves global optimization of the inputs of the decision tree using the bee colony algorithm to identify the informative features. The method initializes the feature space spanned by the dataset. Less relevant features are suppressed according to the information they contribute to the decision making using an artificial bee colony algorithm. Experiments are conducted with two breast cancer datasets and six datasets from the public data repository. Experimental results demonstrate that the proposed method effectively reduces the dimensions of the dataset and achieves superior classification accuracy using the selected features. (C) 2018 Elsevier B.V. All rights reserved.

关键词： bee colony algorithm Decision tree Feature selection Dimensionality reduction

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Synergistic path planning for ship-deployed multiple UAVs to monitor vessel pollution in ports

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2022年第0期110卷

作者： Shen, Lixin Hou, Yunxia Yang, Qin Lv, Meilin Dong, Jing-Xin Yang, Zaili Li, Dongjun Dalian Maritime Univ Maritime Econ & Management Coll Dalian 116026 Peoples R China Newcastle Univ Business Sch 5 Barrack Rd Newcastle Upon Tyne NE1 4SE England Liverpool John Moores Univ Offshore & Marine Res Inst Liverpool Logist Liverpool England Northumbria Univ Newcastle Business Sch Sutherland Bldg Ellison Pl Newcastle Upon Tyne NE1 8ST England

Traditionally, vessel air emissions are monitored onboard vessels or at fixed points at sea. These methods are not cost-effective for implementing emission control laws that address air pollution monitoring of vessels travelling over a large body of water. Unmanned aerial vehicles (UAVs) equipped with pollution monitoring sensors are becoming a research focus. However, due to battery capacity constraints, the monitoring scope of UAVs is still not optimal. Thus, using a ship (such as a patrol ship) as a UAV mobile supply base can overcome battery limitations and increase monitoring coverage. This paper investigates the joint routing and scheduling problem of ship -deployed multiple UAVs (SDMUs) for the monitoring of pollution from vessels. The artificial bee colony (ABC) algorithm based on simulated annealing is employed to minimize the total monitoring time. The model and solution algorithm are verified by real-time dynamic vessel data from Tianjin Port.

关键词： UAV Vessel air pollution Two -level path planning bee colony algorithm

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Countering Cyberattacks against Intelligent Bioinspired Systems Based on FANET

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2020年第8期54卷 822-828页

作者： Pavlenko, E. Yu Stepanov, M. D. Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

It is proposed to use a bee colony algorithm for modeling intelligent systems based on self-organizing FANETs (Flying Ad Hoc Networks). A mathematical model of an intelligent fire extinguishing system is developed and possible cyberattacks on it are described and modeled. For each cyberattack scenario, a countermeasure scenario with a strict mathematical description is developed.

关键词： bee colony algorithm cyberattack FANET mathematical model

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Honey formation optimization framework for design problems

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APPLIED MATHEMATICS AND COMPUTATION 2021年 394卷 125815-125815页

作者： Yetgin, Zeki Abaci, Huseyin Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey Adnan Menderes Univ Engn Fac Dept Comp Engn Aydin Turkey

In this paper, Honey Formation Optimization (HFO) Framework is proposed for design problems where the functions of the objective functions are known or under design. We applied HFO framework to three ABC variants, namely, ABC, GABC and ABC/Best/1 by adapting their local search strategies into HFO. Thus, 6 algorithms consisting of 3 ABC variants (ABC, GABC, ABC/Best/1) and 3 HFO variants (HFO-ABC, HFO-GABC, HFO-ABC/Best/1) are comparatively studied. The HFO-GABC and HFO-ABC/Best/1 algorithms are our new contributions to the literature. The HFO considers a food source consisting of multiple components, and models the honey formation inside bees as a process of mixing the components with their special enzymes according to a honey function. Each food source has its own associated honey form. Thus, HFO looks for the best honey form. HFO requires component design where component function measures the amount of component (component fitness) in a food source and the honey function maps the components to honey amount (quality) to measure the honey fitness. HFO generalises the ABC by considering worker bees that exploit only the selected components of the food sources. In this paper, the 6 algorithms are compared on the basis of 14 benchmark functions, alongside demonstration of the component design. The results show that HFO variants increase the exploitation and exploration abilities of ABC variants significantly. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Honey formation optimisation Component design Honey function bee colony algorithm Objective function decomposition

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A fuzzy multi-objective optimization model for a sustainable reverse logistics network design of municipal waste-collecting considering the reduction of emissions

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JOURNAL OF CLEANER PRODUCTION 2021年 318卷 128577-128577页

作者： Hashemi, Seyed Emadedin Islamic Azad Univ Arak Branch Dept Ind Engn Arak Iran

The most important reason for waste collection is the protection of the environment and the health of the population. Reverse logistics is applied in the sustainable management of municipal waste and is used in the collection, recycling and reuse, as well as the reduction of consumables and environmental compatibility. One of the challenges of sustainable management is costs and customer demand must be considered simultaneously. In this paper, we try to address a comprehensive approach by applying fuzzy mathematical programming to design a multi-objective model for a reverse logistics network. To cover all aspects of this system, we tried to minimize the cost of facility construction, vehicle fuel and environmental damage from the emission of polluting gases, as well as minimize the sum of the ratio of unanswered customer demand to the amount of their demand for all periods, as objective functions of the model. In order to obtain solutions on the Pareto front, a customized multi-objective genetic algorithm (NSGAII) and a customized bee colony algorithm (BCO) were applied. The results of the two algorithms according to the indicators of quality comparison, spacing, diversification and solution time have been compared. The results showed that in all cases, the bee colony algorithm was better able to explore and extract the area to a feasible solution and to achieve near-optimal answers. In terms of spacing metric and resolution time, the genetic algorithm performed better than the bee algorithm.

关键词： Reverse logistics Municipal waste-collecting Reduction of emissions Fuzzy mathematical programming Multi-objective metaheuristic algorithm NSGA II bee colony algorithm

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DART algorithm for constrained optimisation with applications in suspension design

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INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS 2019年第2期26卷 136-157页

作者： Jabeen, Syeda Darakhshan Mukherjee, Rathindra Nath Saha, Jyotirmoy Birla Inst Technol Mesra Dept Math Ranchi 835215 Bihar India Burdwan Univ Dept Math Burdwan 713104 W Bengal India Jadavpur Univ Dept Prod Engn Kolkata 700032 India

We propose a new hybrid algorithm with application of suspension designs problem. The algorithm is based on divide-and-rule technique (DART) governed by artificial bee colony (ABC) algorithm. A divide phase splits the search region into subregions of smaller size and a ruling phase allows bees to rule over the selected subregion and look for a new region with best food source. The DART process is recursively applied to discover higher fitness subregion producing the optimal solution to the problem. Moreover, a new search strategy has been implemented to bias the bees towards feasible solution. A suspension problem has been formulated as a nonlinear constrained optimisation problem where the weighted sum of sprung mass jerks and their deflections are minimised under technological constraints. By the proposed algorithm the optimal passive suspension parameters have been obtained from the half car model to minimise the level of vibration and enhance passengers comfort. Moreover, the results obtained by model simulations with existing and optimised suspension parameters are compared.

关键词： suspension half car optimisation bee colony algorithm ISO 2631 standards

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A novel power optimized hybrid renewable energy system using neural computing and bee algorithm

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AUTOMATIKA 2019年第3期60卷 332-339页

作者： Muthukumar, R. Balamurugan, P. Mt Zion Coll Engn & Technol Dept Elect & Elect Engn Pudukkottai India

With rapid depletion of non-renewable energy resources or the fossil fuels like coal, petroleum etc., there has been a significant shift in innovations towards exploiting and tapping of energy from renewable energy resources like sun, bio gas, wind etc., E Off late, there has been an increased research towards combined or hybrid integrated energy generation systems based on renewable resources like sun-wind, sun-biogas etc., These hybrid systems effectively address the past demerits observed in standalone systems which could provide substantial power only during specific periods and seasons. For example, solar power would be much reduced during the night time. Hence hybrid systems effectively counteract this issue as the lack of stability in one system is well compensated by the other. This research paper proposes an optimized hybrid PV-wind power generation system with optimization towards maximization of power generated from the system with the help of neural architecture and bee colony algorithm. The proposed system has been implemented and tested for a wide range of solar irradiances and wind velocities and maximum and stable power generation has been observed when compared to existing techniques.

关键词： Renewable sources of energy hybrid systems optimization algorithms bee colony algorithm maximum power point tracking

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A joint adaptive evolutionary model towards optical image contrast enhancement and geometrical reconstruction approach in underwater remote sensing

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SN APPLIED SCIENCES 2019年第10期1卷 1242页

作者： Moghimi, Mohammad Kazem Mohanna, Farahnaz Univ Sistan & Baluchestan Dept Commun Engn Zahedan Iran

Nowadays, the underwater optical imaging is used by a wide range of sciences including marine scientists for studying underwater structures and organisms, oil and gas companies, telecommunications to monitor fuel transmission lines and cables, and military centers to detect the sea mines and submarines. Images with non-uniform brightness are visually limited due to low ambient light during imaging or excessive exposure, fogging, hazing, or a combination of these factors. As a result, a process of imaging improvement is highly complex with some conditions. Particularly, this issue is harmful for the underwater environments as well as its highly complexity. In this paper, we improve the underwater images from different environments by exploiting image processing techniques including elimination of fogging in the image, and improvement of contrast with combining super resolution methods which result in significant enhancement of the image quality. To remove fogging in the image enhancement method, a variational-based fusion method was proposed without increasing or decreasing image resolution to increase the adaptability of non-uniform backlight images. This approach is optimized by the bee colony algorithm. Additionally, weighting coefficient selection method is used for super resolution of image after adjusting contrast. The main reason for using hybrid contrast techniques includes the adaptive enhancement and color correction. The bee colony algorithm and weighting coefficient in super resolution due to the lack of hybrid methods in visually improved water images has been the subject of recent researches. The hybrid set has provided more effect on different images at various conditions. Having applied the algorithm set for low-quality visual image set of underwater, some analyses such as PSNR, MSE and SSIM are tested. Compared to similar solutions, the quality is significantly improved and highlighted the exhaustive operation of the algorithm. Applying the presented

关键词： Underwater images Adaptive contrast enhancement bee colony algorithm Super resolution Weighting coefficient selection

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Optimal Placement and Sizing of Distributed Energy Generation in an Electrical Network Using the Hybrid algorithm of bee Colonies and Newton Raphson

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Journal of Power and Energy Engineering 2020年第6期8卷 9-21页

作者： Fissou Filbert Amigue Salomé Ndjakomo Essiane Steve Perabi Ngoffe Aristide Tolok Nelem Laboratory Technology and Applied Sciences University of Douala Douala Cameroon Laboratory of Applied Engineering and Biotechnology HTTTC of EBOLOWA University of Yaoundé 1 Yaoundé Cameroon

Distributed generation (DG) is gaining in importance due to the growing demand for electrical energy and the key role it plays in reducing actual energy losses, lowering operating costs and improving voltage stability. In this paper, we propose to inject distributed power generation into a distribution system while minimizing active energy losses. This injection should be done at a grid node (which is a point where energy can be injected into or recovered from the grid) that will be considered the optimal node when total active losses in the radial distribution system are minimal. The focus is on meeting energy demand using renewable energy sources. The main criterion is the minimization of active energy losses during injection. The method used is the algorithm of bee colony (ABC) associated with Newtonian energy flow transfer equations. The method has been implemented in MATLAB for optimal node search in IEEE 14, 33 and 57 nodes networks. The active energy loss results of this hybrid algorithm were compared with the results of previous searches. This comparison shows that the proposed algorithm allows to have reduced losses with the power injected that we have found.

关键词： Optimization Distributed Power Generation bee colony algorithm Newton Raphson

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Artificial bee colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety 3rd

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3rd International Conference on Machine Learning, Optimization, and Data Science (MOD)

作者： Lazzerini, Beatrice Pistolesi, Francesco Univ Pisa Dept Informat Engn Largo Lucio Lazzarino 1 I-56122 Pisa Italy

ISBN: (纸本)9783319729268;9783319729251

Worldwide, just under 5,800 people go to work every day and do not return because they die on the job. The groundbreaking Industry 4.0 paradigm includes innovative approaches to improve the safety in the workplace, but Small and Medium Enterprises (SMEs) - which represent 99% of the companies in the EU - are often unprepared to the high costs for safety. A cost-effective way to improve the level of safety in SMEs may be to just reassign employees to tasks, and assign hazardous tasks to the more cautious employees. This paper presents a multi-objective approach to reallocate the personnel of a company to the tasks in order to maximize the workplace safety, while minimizing the cost, and the time to learn the new tasks assigned. Pareto-optimal reallocations are first generated using the Non-dominated Sorting artificial bee colony (NSBC) algorithm, and the best one is then selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach was tested in two SMEs with 11 and 25 employees, respectively.

关键词： bee colony algorithm Occupational safety and health Multi objective optimization Personnel reallocation Risk perception TOPSIS

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