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Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

EXPERT SYSTEMS WITH APPLICATIONS 2011年第11期38卷 13785-13791页

作者： Horng, Ming-Huwi Natl PingTung Inst Commerce Dept Comp Sci & Informat Engn Pingtung City 900 Taiwan

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu's method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Honey bee mating optimization Hybrid cooperative-comprehensive learning based PSO algorithm Fast Otsu's method artificial bee colony algorithm

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A new design method based on artificial bee colony algorithm for digital IIR filters

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2009年第4期346卷 328-348页

作者： Karaboga, Nurhan Erciyes Univ Fac Engn Dept Elect Elect Engn TR-38039 Kayseri Turkey

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm. (c) 2009 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Digital IIR filters System identification Swarm-based optimization artificial bee colony algorithm

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Dynamic green bike repositioning problem - A hybrid rolling horizon artificial bee colony algorithm approach

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2018年第May期60卷 119-136页

作者： Shui, C. S. Szeto, W. Y. Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat Shenzhen Peoples R China

This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO2 emission cost of the repositioning vehicle over an operational period. The problem determines the route and the number of bikes loaded and unloaded at each visited node over a multi-period operational horizon during which the cycling demand at each node varies from time to time. To handle the dynamic nature of the problem, this study adopts a rolling horizon approach to break down the proposed problem into a set of stages, in which a static bike repositioning sub-problem is solved in each stage. An enhanced artificial bee colony (EABC) algorithm and a route truncation heuristic are jointly used to optimize the route design in each stage, and the loading and unloading heuristic is used to tackle the loading and unloading sub-problem along the route in a given stage. Numerical results show that the EABC algorithm outperforms Genetic algorithm in solving the routing sub-problem. Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO2 emission cost. Numerical studies are also performed to illustrate the effects of the weight and the loading and unloading times on the two objective values and the tradeoff between the two objectives. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Green bike repositioning problem Dynamic bike repositioning problem Rolling horizon approach artificial bee colony algorithm Vehicle emissions

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On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation

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INFORMATION SCIENCES 2015年第0期291卷 115-127页

作者： Mernik, Marjan Liu, Shih-Hsi Karaboga, Dervis Crepinsek, Matej Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

artificial bee colony (ABC) is a Swarm Intelligence algorithm that has obtained meta-heuristic researchers' attention and favor over recent years. It comprises good balance between exploitation (employed bee phase and onlooker bee phase) and exploration (scout bee phase). As nowadays, more researchers are using ABC and its variants as a control group to perform comparisons, it is crucial that comparisons with other algorithms are fair. This paper points to some misapprehensions when comparing meta-heuristic algorithms based on iterations (generations or cycles) with special emphasis on ABC. We hope that through our findings this paper can be treated as a beacon to remind researchers to learn from these mistakes. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence artificial bee colony algorithm Experiment replication

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A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm

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APPLIED SOFT COMPUTING 2018年 68卷 360-376页

作者： Liu, Hong Xu, Bin Lu, Dianjie Zhang, Guijuan Shandong Normal Univ Sch Informat Sci & Engn 88 Wenhua East Rd Jinan Shandong Peoples R China Shandong Prov Key Lab Novel Distributed Comp Soft Jinan Shandong Peoples R China

This paper proposes a new path planning approach for emergency evacuation simulation. This technique combines the Extended Social Force Model (ESFM) and the Improved artificial bee colony (IABC) algorithm to enhance the visual realism and improve the efficiency of crowd evacuation. In the ESFM, we introduce a visual parameter to the original SFM and obtain the anisotropic psychological force rather than the isotropic one in the SFM so as to better fit crowd behaviors, such as long-range obstacle avoidance and self-organizing group formation. In addition, the IABC algorithm is proposed to improve the evacuation efficiency and provide support for building design and evacuation management by employing the strategies of grouping and exit selection. The algorithm uses the evacuation time of the individuals as the evaluation metric. If an exit is overcrowded and congested, the individuals will assess the degree of congestion, estimate the time needed to escape, and determine whether to select a farther exit for escape. By selecting the optimal exit and avoiding congestion, the evacuation efficiency can be improved. We have simulated the crowd evacuation with our new path planning approach via a crowd evacuation simulation system. The results show the effectiveness of our method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Crowd evacuation Simulation model Path planning Social force model artificial bee colony algorithm

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Service Composition Instantiation Based on Cross-Modified artificial bee colony algorithm

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China Communications 2016年第10期13卷 233-244页

作者： Lei Huo Zhiliang Wang School of Automation University of Science and Technology Beijing School of Computer and Science University of Science and Technology Beijing

Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified artificial bee colony algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

关键词： optimization of service composition optimal service instantiation artificial bee colony algorithm swarm algorithm cross strategy

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Strategic Bidding in Transmission Constrained Electricity Markets Using artificial bee colony algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第16期40卷 1768-1788页

作者： Jain, A. K. Srivastava, S. C. Singh, S. N. Srivastava, L. Indian Inst Technol Kanpur Dept Elect Engn Kanpur 802016 Uttar Pradesh India Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

This article presents a methodology for a supplier's optimal bidding strategy considering network constraints and rivals' bidding behaviors. This has been formulated as a bi-level multi objective optimization problem, in which the lower-level problem represents the market clearing process by the system operator for minimization of customers' payments. The upper-level problem is a multi-objective formulation that simultaneously considers profit maximization and risk minimization. Pareto-optimal solutions of the bi-level multi-objective optimization problem have been obtained using the epsilon-constraint method. A fuzzy decision-making process has been used to derive the most efficient solution among the Pareto-optimal solutions. Upper-level objective functions are highly non-linear;therefore, the artificial bee colony algorithm, a relatively new heuristic approach, has been applied to obtain the best solution to the bi-level multi objective optimization problem for a day-ahead hourly market. The effectiveness of the proposed method has been established on a 2-bus system and the IEEE-30 bus test system. The results, obtained using the artificial bee colony algorithm, have been compared with those obtained using a genetic algorithm based approach.

关键词： artificial bee colony algorithm bi-level multi objective optimization problem congestion management epsilon-constraint method rivals' bidding behavior optimal bidding strategy

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Adaptive fuzzy-based message dissemination and micro-artificial bee colony algorithm optimised routing scheme for vehicular ad hoc network

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IET COMMUNICATIONS 2020年第6期14卷 994-1004页

作者： Patil, Saurabh Dadasaheb Ragha, Lata Mumbai Univ Terna Engn Coll Nerul Navi Mumbai India Mumbai Univ Fr CRIT Comp Engn Vashi Navi Mumbai India

Nowadays, researches focus on providing services for the emergency vehicle and also providing potential applications. Vehicular ad hoc networks (VANETs) are considered as an emerging research area. In heavily populated cities, the emergency vehicles not being able to reach their destination (hospitals) on time due to the slow-moving traffic. Hence, it results in loss of lives many times. Therefore, the important concern in this area is reaching the destination in an emergency situation. In this study, an adaptive fuzzy-based message dissemination and micro artificial bee colony algorithm (MABC) optimised routing scheme for VANET is proposed. An adaptive fuzzy-based multi-criteria decision-making approach is adopted for the cluster head selection. This method combines the fuzzy decision-making trial and evaluation laboratory model-based analytical network process (ANP) termed as fuzzy DANP (i.e. Decision Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Processes (ANP)) and fuzzy Visekriterijumska Optimizacija I kompromisno Resenje methods for optimal decision making. For emergency broadcast, a vehicular emergency dissemination protocol is used in vehicle-to-vehicle communication. Finally, an MABC is used to select the best minimum route from start to the endpoint. The proposed method is simulated in MATLAB, and the results show outstanding improvements compared to other existing approaches.

关键词： decision making vehicular ad hoc networks fuzzy set theory artificial bee colony algorithm telecommunication network routing telecommunication traffic optimised routing scheme vehicular ad hoc network emergency vehicle destination emergency situation microartificial bee colony algorithm MABC VANET multicriteria decision-making approach cluster head selection fuzzy decision-making trial evaluation laboratory model-based analytical network process optimal decision making emergency broadcast vehicular emergency dissemination protocol vehicle-to-vehicle communication minimum route adaptive fuzzy-based message dissemination

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A Novel Cloning Template Designing Method by Using an artificial bee colony algorithm for Edge Detection of CNN Based Imaging Sensors

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SENSORS 2011年第5期11卷 5337-5359页

作者： Parmaksizoglu, Selami Alci, Mustafa Erciyes Univ Fac Engn Kayseri Turkey

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an artificial bee colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods.

关键词： cellular neural networks edge detection artificial bee colony algorithm real time imaging sensors

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Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2013年第2期64卷 293-311页

作者： Lin, S-W Ying, K-C Chang Gung Univ Dept Informat Management Tao Yuan Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 10608 Taiwan

The order acceptance and scheduling (OAS) problem is an important topic for make-to-order production systems with limited production capacity and tight delivery requirements. This paper proposes a new algorithm based on artificial bee colony (ABC) for solving the single machine OAS problem with release dates and sequence-dependent setup times. The performance of the proposed ABC-based algorithm was validated by a benchmark problem set of test instances with up to 100 orders. Experimental results showed that the proposed ABC-based algorithm outperformed three state-of-art metaheuristic-based algorithms from the literature. It is believed that this study successfully demonstrates a high-performance algorithm that can serve as a new benchmark approach for future research on the OAS problem addressed in this study. Journal of the Operational Research Society (2013) 64, 293-311. doi:10.1057/jors.2012.47;published online 9 May 2012

关键词： order acceptance and scheduling problem single machine artificial bee colony algorithm sequence-dependent setup times

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