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Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2014年 67卷 235-263页

作者： Szeto, W. Y. Jiang, Y. Univ Hong Kong Dept Civil Engn Hong Kong Hong Kong Peoples R China

This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Transit route and frequency setting problem Bus network design Bi-level programming artificial bee colony algorithm Mixed integer program Matheuristics

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Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm

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APPLIED OCEAN RESEARCH 2017年 68卷 249-261页

作者： Zhu, Man Hahn, Axel Wen, Yuan-Qiao Bolles, Andre Carl von Ossietzky Univ Oldenburg Comp Sci D-26121 Oldenburg Germany Wuhan Univ Technol Sch Nav Wuhan 430063 Hubei Peoples R China Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China Inst Informat Technol D-26121 Oldenburg Germany

The 6 degrees of freedom (DOE) model with a high degree of complexity for capturing ship dynamics is generally able to track the nonlinear and coupling dynamics of ships. However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control. Therefore, simplified ship dynamic models within allowed accuracy are essential. This paper simplified the 6 DOF nonlinear dynamic model of ships into two decoupled models including the speed model and the steering model through reasonable assumptions. Those models were tested through maneuvering simulations of a container ship with a 4 DOF dynamic model. Support vector machines (SVM) optimized by the artificial bee colony algorithm (ABC) was used to identify parameters of speed and steering models by analyzing the rudder angle, propeller shaft speed, surge and sway velocities, and yaw rate from simulated data extracted from a series of maneuvers made by the container ship. Comparisons with the first order linear and nonlinear Nomoto models show that the simplified nonlinear steering model can capture more complicated dynamics and performs better. Additionally, comparisons among three different parameter identification methods demonstrate similar identification results but the different performance involving the applicability and effectiveness. SVM optimized by ABC is relatively convenient and effective for parameter identification of ship simplified dynamic models. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Nonlinear ship dynamics Simplified ship models Large ships Support vector machines artificial bee colony algorithm Parameter identification

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A Reinforcement Learning Driven artificial bee colony algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第4期20卷 2305-2320页

作者： Zhao, Fuqing Wang, Zhenyu Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The distributed heterogeneous factory system is a typical scenario in the manufacturing industry. A distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup times (DHNWFSP-SDST) is studied in this paper. The differences in factory configuration and transportation time are considered in DHNWFSP-SDST. A mixed-integer linear programming (MILP) model is constructed and an artificial bee colony algorithm (ABC) with Q-learning (QABC) is proposed to address the DHNWFSP-SDST. Heuristic methods named NEH_H and DHHS are designed to construct potential initial candidates for the population. The neighborhood structures based on the job blocks are introduced in QABC to explore the solution space during the evolution processes. The Q-learning mechanism is employed to select neighborhood structures via empirical knowledge in the operation processes. The speed-up methods to accelerate the evaluation of the obtained neighborhood are designed to reduce the computation time of the QABC. The experimental results show that the QABC is a potential algorithm to address the DHNWFSP-SDST.

关键词： Heterogeneous flowshop scheduling sequence-dependent setup times artificial bee colony algorithm Q-learning, neighborhood structure

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A novel artificial bee colony algorithm with Powell's method

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APPLIED SOFT COMPUTING 2013年第9期13卷 3763-3775页

作者： Gao, Wei-feng Liu, San-yang Huang, Ling-ling Xidian Univ Sch Sci Xian 710071 Shannxi Peoples R China China Univ Petr Sch Sci Qingdao 266580 Peoples R China

artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Evolutionary algorithms artificial bee colony algorithm Search equation Powell's method

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A Source-code Aware Method for Software Mutation Testing Using artificial bee colony algorithm

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2022年第3期38卷 289-302页

作者： Arasteh, Bahman Imanzadeh, Parisa Arasteh, Keyvan Gharehchopogh, Farhad Soleimanian Zarei, Bagher Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Islamic Azad Univ Shabestar Branch Dept Comp Engn Shabestar Iran

The effectiveness of software test data relates to the number of found faults by the test data. Software mutation test is used to evaluate the effectiveness of the software test methods and is one of the challenging fields of software engineering. In order to evaluate the capability of test data in finding the program faults, some syntactical changes are made in the program source code to cause faulty program;then, the generated mutants (faulty programs) and original program are executing with the corresponding test data. One of the main drawbacks of mutation testing is its computational cost. Indeed, high execution time of mutation testing is a challenging research problem. Reducing the time and cost of mutation test is the main objective of this paper. In the traditional mutation methods and tools the mutants are injected randomly in each instructions of a program. Meanwhile, in the real-world program, the probability of fault occurrences in the simple locations (instructions and data) of a program is negligible. With respect to the 80-20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. In the first stage of the proposed method, artificial bee colony optimization algorithm is used to identifying the most fault prone paths of a program;in the next stage, the mutation operators (faults) are injected only on the identified fault-prone instructions and data. Regarding the results of conducted experiments on the standard benchmark programs, Compared to existing methods, the proposed method reduces 28.10% of the generated mutants. Reducing the number of generated mutants will reduce the cost of mutation testing. The traditional mutation testing tools (Mujava, Muclipse, Jester, Jumble) can perform the mutation testing with a lower cost using the method presented in this study.

关键词： Software mutation testing Mutation reduction Fault-prone test paths artificial bee colony algorithm Mutation score

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artificial bee colony algorithm with adaptive covariance matrix for hearing loss detection

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KNOWLEDGE-BASED SYSTEMS 2021年 216卷 106792-106792页

作者： Yang, Jingyuan Cui, Jiangtao Zhang, Yu-Dong Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Univ Leicester Dept Informat Leicester LE1 7RH Leics England

artificial bee colony algorithm (ABC) is an efficient and popular evolutionary algorithm (EAs), which has been attracted wide attention by researchers, and improved ABC with various characteristics (ABCs) have been proposed. It is widely acknowledged that the search operator is the core element in the performance of ABC. However, the generally designed search operators of ABCs are rotation-variable processes and are dependent mainly on the natural coordinates and, as a result, the performance of those ABC is limited. In this paper, the mathematical characteristic of the search operator is deeply analyzed, and on this basis, ABC with adaptive covariance matrix (ACoM-ABC) is proposed, the adaptive covariance matrix (ACoM) is used to establish a proper coordinates by making use of the population distribution information, which can relieve the dependence of ABC on the coordinates to a certain extent and improve the exploitation capability. To balance the exploitation and exploration abilities of ABC, the search operators of ABC are implemented on eigen coordinates and natural coordinates Then, to estimate the performance of ACoM-ABC, which compares with six ABCs and other six EAs, and tests on CEC2014. The excellent experimental result shows that ACoM-ABC is an efficient and outstanding algorithm. Moreover, the proposed algorithm is applied to hearing loss detection, and the experiment result shows that the overall accuracy is 96.67%, which higher than the other five state-of-the-art approaches about 1%. Therefore, the ACoM-ABC has the practicability for realistic problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Rotation-variable process Adaptive covariance matrix Eigen coordinates Hearing loss detection

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artificial bee colony algorithm based modified circular-shaped compact hybrid fractal antenna for industrial, scientific, and medical band applications

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2022年第3期32卷 e22994-e22994页

作者： Kaur, Manpreet Sivia, Jagtar Singh Punjabi Univ Yadavindra Coll Engn Guru Kashi Campus Bathinda Punjab India

This paper explores the unique design of a circular-shaped compact hybrid fractal antenna (CCHFA) with defected ground plane for industrial, scientific, and medical band applications. The designed antenna is built on an easily available 1.6 mm thick, FR4 epoxy substrate of size 37.5 x 37.5 mm(2). It utilizes a hybrid approach that involves the amalgamation of two distinct circular fractal patterns. A 50 omega feed line of width 2.8 mm is connected with the designed patch. In this design, the ground plane dimension 'L-g' is optimized specifically with an artificial bee colony (ABC) algorithm. To realize the effectiveness of the proposed design, the performance is practically evaluated through experimental testing. The proposed antenna in its final version, practically offers a bandwidth of 87.2 MHz (3.52%) and gain of 8.1 dB at the claimed resonance. Radiation patterns provided by the proposed CCHFA are arbitrary omnidirectional/bidirectional and exhibit sufficient stability. The simulated and experimental results demonstrate the applicability of ABC algorithm in the devices designed for biomedical purposes. Furthermore, an exhaustive parametric evaluation has also been conducted to examine the effect of variations in few geometrical parameters on the performance of the designed antenna.

关键词： artificial bee colony algorithm feeding techniques gain hybrid fractal antenna ISM band applications resonant frequency

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An enhanced artificial bee colony algorithm with adaptive differential operators

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APPLIED SOFT COMPUTING 2017年 58卷 480-494页

作者： Liang, Zhengping Hu, Kaifeng Zhu, Quanxiang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

artificial bee colony algorithm (ABC) has been shown to be very effective to solve global optimization problems (GOPs). However, ABC performs well in exploration but relatively poorly in exploitation resulting in a slow convergence when it is used to handle complex GOPs. Differential evolution (DE) benefits from its differential operators, namely mutation operator and crossover operator, which could perturb multiple variables simultaneously and has shown a fast convergence speed. In order to improve ABC's exploitation ability and accelerate its convergence, in this paper, we propose an enhanced ABC algorithm named ABCADE, which remedy the limitation of ABC by exploiting the advantage of differential operators. Particularly, in ABCADE, the employed bees employ differential operators to produce candidate solutions with an increasing probability, and the two important parameters (scale factor F and crossover rate CR) of differential operators are adaptively adjusted through Gaussian distribution. Moreover, to significantly differentiate the good solutions and bad solutions in a population, and put more effort in the exploitation around the good solutions, we design a new selection probability method for onlooker bees. To verify the performance of ABCADE, we compare ABCADE with other representative state-of-the-art ABC and DE algorithms, the comparison results on a set of 22 benchmark functions with various dimension sizes demonstrate that ABCADE obtains superior or comparable performance to other algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Differential operators Selection probability Global optimization

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Enhancing of artificial bee colony algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 496-510页

作者： Kruekaew, Boonhatai Kimpan, Warangkhana King Mongkuts Inst Technol Ladkrabang Dept Comp Sci Bangkok 10520 Thailand

This paper proposes the combination of Swann Intelligence algorithm ofartific a! her colony caalh heuristic scheduling algorithm, called Heuristic `task Scheduling with artificial lice colony (UMW). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system With HABC was compared to that supplemented with other swarm intelligence algorithms: Ant colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can he concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJT) outperformed those with ACO, PSO, and IPSO. (C) 2020 The Authors. Published by Atlantis Press SARI.

关键词： artificial bee colony algorithm Cloud computing Scheduling algorithms Load balance Resource management Distribution

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Research of the utilization efficiency of non-flood season flood resources by artificial bee colony algorithm based on multi-strategy hybrid search

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WATER SUPPLY 2023年第5期23卷 1987-2000页

作者： Li, Jie Yang, Zhou Liu, Zhao Liu, Hong-zhi Tian, Yang-jun Changan Univ Sch Water & Environm Xian 710054 Shaanxi Peoples R China Changan Univ Key Lab Subsurface Hydrol & Ecol Effects Arid Reg Minist Educ 126 Yanta Rd Xian 710054 Peoples R China Sichuan Huaneng Baoxing River Power Co Ltd Yaan 625000 Peoples R China Hanjiang To Weihe River Valley Water Divers Projec Xian 710010 Shaanxi Peoples R China

This study aims to investigate the integrated optimization of long-term non-flood season flood dispatching and power generation dispatching and improve the utilization efficiency of non-flood season flood resources. In this study, an artificial bee colony (ABC) algorithm based on a multi-strategy mixed search is supplemented with variable time period characteristics to construct an optimal reservoir operation model with a maximum generating capacity under multiple constraints in a variable time period. The multi-year non-flood season flood operation of the Wanan Reservoir is considered as an example. After optimization, the average annual power generation in the non -flood season was increased by 14.23 million kWh. The average utilization rate of water resources was increased by 3% over the studied period, and the utilization of flood resources was improved. The ABC optimization algorithm based on a multi-strategy hybrid search could alleviate the contradiction between the selection of the time interval step and the calculation accuracy and convergence speed, and it effectively improved the optimization efficiency and development ability of the standard ABC optimization algorithm. These results can provide suggestions for the improvement of the comprehensive benefits of reservoirs with non-flood floods as an important water resource.

关键词： artificial bee colony algorithm multi-strategy mixed search optimal dispatch of flood control power generation variable-period step size Wanan reservoir

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