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6th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhao Ming Song Xiao-Yu Gao Yi-Chen Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang 110168 Liaoning Peoples R China Shenyang Gas Grp Co Ltd Informat Technol Management Off Shenyang 110000 Liaoning Peoples R China

ISBN: (纸本)9781479983537

When large scale disaster occurs suddenly, allocating scheme of the first batch of "life" emergency supplies directly determines rescue effect. To make lack loss evaluation of the supplies accordant with actual cases, this paper proposed nonlinear loss evaluation function, based on which constructed multi to multi constrained scheduling model with multi objectives. To improve the performance of artificial bee colony algorithm, this paper proposed following schemes: food sources initialization based on converse food source.;reverse learning and comprehensive learning added into bee searching procedure. Experiments results on random data of three scales show that non dominated front solutions set solved by the improved algorithm is more diverse, wide spread and uniform.

关键词： nonlinear loss emergency scheduling model constrained multi-objective optimization artificial bee colony algorithm reverse learning comprehensive learning

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An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

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ENGINEERING COMPUTATIONS 2023年第9/10期40卷 2453-2482页

作者： Deliktas, Derya Aydin, Dogan Utahya Dumlupinar Univ Fac Engn Dept Ind Engn Kutahya Turkiye Izmir Katip Celebi Univ Dept Comp Engn Fac Engn & Architecture Izmir Turkiye

Purpose - Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time ***/methodology/approach - This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art *** - The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art ***/value - This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

关键词： Simple assembly line balancing Hyper-heuristic artificial bee colony algorithm irace

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Merged automobile parts maintenance delivery problem using an improved artificial bee colony algorithm

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SCIENTIA IRANICA 2015年第3期22卷 1258-1270页

作者： Yao, B. Hu, P. Yu, L. Zhang, M. Gao, J. Dalian Univ Technol Sch Automot Engn Dalian 116024 Peoples R China Yanching Inst Technol Sch Int Trade & Commerce Beijing 065201 Peoples R China

The merged automobile parts maintenance delivery problem has attracted interest from merged companies due to reduced delivery costs by collaborative delivery between several automobile part depots. Since the delivery problem is very complex, a Voronoi diagram is adopted to simplify it by splitting customers into several sets, and by attempting to solve it using the artificial bee colony algorithm. To improve the performance of the artificial bee colony algorithm, an adaptive strategy is used to control the proportion of scouts and leaders. Finally, the computational results for 23 benchmark problems indicate that the proposed algorithm is an effective method for solving multi-depot vehicle routing problems. Furthermore, the results of the merged automobile parts maintenance delivery problem also indicate that the improved artificial bee colony algorithm with the Voronoi diagram is feasible for solving this kind of delivery problem. (C) 2015 Sharif University of Technology. All rights reserved.

关键词： Automobile maintenance parts delivery problem Multi-depot vehicle routing problem Voronoi diagram Adaptive strategy artificial bee colony algorithm Merged

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artificial bee colony algorithm for Automatic Leukocytes Segmentation in Histopathological Images 9

Artificial Bee Colony Algorithm for Automatic Leukocytes Seg...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Sharma, Harish Arya, K. V. Saraswat, Mukesh Rajasthan Tech Univ Kota India ABV Indian Inst Informat Technol & Management Gwalior India Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9781479964994

An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drug and encourages for development of automated leukocytes recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images, and shape of the nuclei. Surprisingly, rare efforts have been done to automate the segmentation of leukocytes in various disease models on Hematoxylin and Eosin stained tissue images. The present work proposes a novel method based on artificial bee colony algorithm to segment the leukocytes from the images of mice skin sections stained with Hematoxylin and Eosin staining and acquired at 40 x magnification. The results show that the proposed method outperforms when compared with the well known segmentation methods.

关键词： diseases image segmentation medical image processing optimisation pharmaceutical technology shape recognition artificial bee colony algorithm automated leukocytes recognition system automatic leukocytes segmentation disease models drug discovery eosin stained tissue images hematoxylin stained tissue images histopathological images mice skin sections nuclei shape pharmaceutical companies tissue images Accuracy Drugs Image segmentation Mice Optimization Skin Tin image segmentation artificial bee colony algorithm Leukocytes pharmaceutical technology shape recognition Medical image processing Disease Model Drug Discovery Tin Pharmaceutical Preparations segmentation images skin Pattern recognition Segmentation

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artificial bee colony algorithm for Multi-Target Tracking in Mobility Sensor Networks

Artificial Bee Colony Algorithm for Multi-Target Tracking in...

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International Conference on Electronics and Communication Systems (ICECS)

作者： Kumar, S. Barath Prakash, B. Arun Christ King Engn Coll Dept Elect & Commun Engn Coimbatore Tamil Nadu India Christ King Engn Coll Dept ECE Coimbatore Tamil Nadu India

ISBN: (纸本)9781479923205

In Mobile Sensor Networks, it is important to manage the mobility of the nodes and optimized computation in order to improve the performance and lifetime of the network. Existing methods dealt about only Single Target tracking methods for these criteria. In this paper, Multi-Target Tracking system using artificial bee colony (ABC) Optimization algorithm was proposed to meet these requirements. In this proposed method, current positions of the targets are estimated and the next positions are predicted. Estimation and Prediction are done by using Interval Analysis. In order to cover multi-target in an optimal way to minimize the total travelled distances by nodes, a better decisions regarding the positions where the sensor is able to move upon is done by optimization techniques. Then assigning, each mobile node one new location within the computed set using the ABC Optimization techniques. In artificial Intelligence, bee colony is the only one approach which can be applied to multi-nodal Optimization problem. This technique uses an ABC Optimization algorithm which can be better suit for unconstrained problems. Simulation results on the well-known benchmark functions, shows the efficiency and effectiveness of the proposed algorithm.

关键词： artificial bee colony algorithm artificial Intelligence Interval Analysis Mobile Sensor Networks Mobility management Multi-nodal Optimization Multi-target tracking

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A hybrid artificial bee colony algorithm for numerical function optimization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2015年第10期26卷 1550109-1550109页

作者： Alqattan, Zakaria N. Abdullah, Rosni Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

artificial bee colony (ABC) algorithm is one of the swarm intelligence algorithms;it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

关键词： Swarm intelligence algorithms artificial bee colony algorithm particle swarm optimization numerical function optimization

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Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the artificial bee colony Optimization algorithm

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SENSORS 2016年第12期16卷 2122-2122页

作者： Naghibi, Fereydoun Delavar, Mahmoud Reza Pijanowski, Bryan Univ Tehran Sch Surveying & Geospatial Engn Coll Engn GIS Dept Tehran *** Iran Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Ctr Excellence Geomat Engn Disaster Management Tehran *** Iran Purdue Univ Dept Forestry & Nat Resources 195 Marsteller St W Lafayette IN 47907 USA

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The artificial bee colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

关键词： urban growth model cellular automata model calibration swarm intelligence artificial bee colony algorithm remote sensing image

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A hybrid whale optimization algorithm with artificial bee colony

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SOFT COMPUTING 2022年第5期26卷 2075-2097页

作者： Tang, Chenjun Sun, Wei Xue, Min Zhang, Xing Tang, Hongwei Wu, Wei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Hunan Prov Key Lab Intelligent Robot Technol Elec Changsha 410082 Hunan Peoples R China

In this paper, the defects and deficiencies of the recently proposed whale optimization algorithm (WOA) are improved. A whale optimization algorithm mixed with an artificial bee colony (ACWOA) is proposed to solve the WOA problems of slow convergence, low precision, and easy to fall into local optimum. The ACWOA algorithm integrates the artificial bee colony algorithm and chaotic mapping, effectively avoiding the local optimal situation and improving the quality of the initial solution. Also, nonlinear convergence factors and adaptive inertia weight coefficients are added to accelerate the convergence rate. To verify the performance of the improved algorithm, 20 benchmark functions and CEC2019 multimodal multi-objective benchmark functions have been used to compare ACWOA with the classical intelligent population algorithms (PSO, MVO, and GWO) and the recent state-of-the-art algorithms (CWOA, HWPSO, and HIWOA) in recent years. The proposed algorithm is applied to two well-known engineering mathematical models and a real application (the quality process control). The experiments show that the ACWOA algorithm has strong competitiveness in convergence speed and solution accuracy and has certain practical value in complex mathematical model scenarios.

关键词： Whale optimization algorithm artificial bee colony algorithm Nonlinear convergence factor Adaptive inertia weight

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An Overview of artificial bee colony algorithm

An Overview of Artificial Bee Colony Algorithm

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2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： Suhan YANG Hongwei JIANG Department of information management Beijing Information Science and Technology University

ISBN: (纸本)9781510806481

A swarm intelligence algorithm develops rapidly, which has solved many large scale complex problems these years. artificial bee colony algorithm is a new swarm intelligence algorithm and gets wide attention for its superior performance, such as, strong global convergence, greedy heuristic search feature and quickly problem solution. The biological background is introduced briefly;By comparing bees foraging behavior with problems solution, modeling thought is given;algorithm model is introduced in detail. Then, research status quo is discussed from two aspects, improvement and application of the algorithm. Also, conclusions are given about artificial bee colony algorithm, and improvement direction and application field are put forward from the weakness analysis of algorithm.

关键词： Swarm Intelligence artificial bee colony algorithm algorithm Principle Research Status Quo

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Emergency Scheduling Optimization Based on Improved artificial bee colony algorithm

Emergency Scheduling Optimization Based on Improved Artifici...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： ZHAO Ming SONG Xiao-Yu Gao Yi-Chen School of Information & Control Engineering Shenyang Jianzhu University Information Technology Management Office Shenyang Gas Group Co.Ltd

When large scale disaster occurs suddenly, allocating scheme of the first batch of "life" emergency supplies directly determines rescue effect. To make lack loss evaluation of the supplies accordant with actual cases, this paper proposed nonlinear loss evaluation function, based on which constructed multi to multi constrained scheduling model with multi objectives. To improve the performance of artificial bee colony algorithm, this paper proposed following schemes: food sources initialization based on converse food source. reverse learning and comprehensive learning added into bee searching procedure. Experiments results on random data of three scales show that non dominated front solutions set solved by the improved algorithm is more diverse, wide spread and uniform.

关键词： nonlinear loss emergency scheduling model constrained multi-objective optimization artificial bee colony algorithm reverse learning comprehensive learning

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