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A hyper-heuristic based artificial bee colony algorithm for k-Interconnected multi-depot multi-traveling salesman problem

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INFORMATION SCIENCES 2018年 463卷 261-281页

作者： Pandiri, Venkatesh Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India

This paper addresses a newly introduced variant of traveling salesman problem, viz. k Interconnected Multi-Depot Multi-Traveling Salesman Problem (k-IMDMTSP). This problem has the potential to address a variety of problems as it is a general problem that can change its characteristics according to the combination of parameter values. In fact, k-IMDMTSP can become an altogether different problem depending on the choice of its parameter values. According to the No Free Lunch Theorem [43], it is not possible to have a general algorithm that can outperform all algorithms across all problems emanating from k-IMDMTSP due to various parameter values. However, an appropriate combination of different algorithms can successfully deal with all such problems emanating from k-IMDMTSP. Here, we have made an attempt in this direction with the help of hyper-heuristics. A hyper-heuristic based artificial bee colony algorithm is proposed for k-IMDMTSP. A new solution encoding scheme is proposed for representing a k-IMDMTSP solution within the proposed approach, and its associated search space is analyzed theoretically. It has been proved that our encoding scheme yields a search space that is considerably smaller in comparison to encoding schemes used previously. Experimental results on standard benchmark instances show that the proposed approach outperforms other stateof-the-art approaches available in literature in terms of both solution quality and running time. (C) 2018 Elsevier Inc. All rights reserved.

关键词： k-Interconnected multi-depot multi-traveling salesman problem Multiple traveling salesman problem artificial bee colony algorithm Hyper-heuristics

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hABCDE: A hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution

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APPLIED MATHEMATICS AND COMPUTATION 2014年 238卷 370-386页

作者： Xiang, Wanli Ma, Shoufeng An, Meiqing Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms which has been proven to be competitive with other population based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which lacks the guidance of better solutions and much more exchange of information between the old solution and new solution. Inspired by gbest-guided ABC (GABC), a new solution search equation with the direction of better solutions, is introduced and combined with the original one. Moreover, many more dimensions of an old solution are perturbed to enhance the level of information exchange between the two solutions (social learning). And then a modified differential evolution (DE) is also incorporated into the modified ABC in view of the fast convergence speed of DE. Subsequently, a new population catastrophe scheme is introduced in order to further achieve better compromise between the exploration and the exploitation. Based on the above explanation, this paper presents a novel hybrid evolutionary algorithm named hABCDE, which integrates a modified ABC and a modified DE to solve numerical optimization problems. Finally, the experimental results tested on a set of 20 benchmark functions show that the hABCDE algorithm can outperform ABC, DE and a few other state-of-the-art DE variants in most cases. (C) 2014 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Differential evolution Chaotic systems Catastrophe Numerical optimization

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Optimal Capacitor Location and Sizing for Reducing the Power Loss on the Power Distribution Systems due to the Dynamic Load of the Electric Buses Charging System using the artificial bee colony algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第4期16卷 1821-1831页

作者： Boonraksa, Terapong Boonraksa, Promphak Marungsri, Boonruang Rajamangala Univ Technol Rattanakosin Sch Elect Engn Salaya Nakhon Pathom Thailand Bangkok Thonburi Univ Sch Elect Engn Bangkok Thailand Suranaree Univ Technol Sch Elect Engn Nakhon Ratchasima Thailand

The modern power distribution system is connected to many loads, affecting the power system reliability and causing more power loss. One of the new loads is the battery charging station for electric vehicles or electric buses. The charging load will have a charge that varies with the operating time of each vehicle. Therefore, this article focuses on optimal capacitor location and sizing for reducing the power loss on the power distribution systems due to the dynamic load of the electric bus system using the artificial bee colony algorithm. The dynamic charging load of 3 types of electric bus systems was applied: Plug-in charging, Pantograph charging, and Battery swapping charging. The simulation results show that the installation of 0.3167 MVar capacitors for Plug-in electric bus systems reduced the power loss by 0.5465 MWh. In comparison, the installation of 0.2893 MVar capacitors for the Pantograph electric bus systems reduces the power loss by 0.5459 MWh. The installation of 0.2922 MVar capacitors for the Battery swapping systems reduces the power loss of 0.5517 MWh. The installation of shunt capacitors can reduce the power distribution system's power loss and the benefit of capacitors installing 1,501.27, 1,443.76, and 1,474.02 USD/year, respectively.

关键词： Shunt capacitors Power distribution systems Dynamic load flow Electric buses artificial bee colony algorithm

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USING AN EFFICIENT artificial bee colony algorithm FOR PROTEIN STRUCTURE PREDICTION ON LATTICE MODELS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第3B期8卷 2049-2064页

作者： Lin, Cheng-Jian Su, Shih-Chieh Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 41170 Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 62102 Taiwan

The well-known artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. The ABC system combines both local and global search methods in an attempt to balance exploration and exploitation processes, and hopefully, it can be successfully applied to solve real-world problems. In the past, the Science Magazine named the protein folding problem, (PFP) as one of the 125 biggest unsolved problems in science. The PEP addresses the question of how the amino acid sequence (A A S) of a specific protein dictates its structure. In the study, we present a modified ABC (MABC) algorithm for the PEP with both 2D and 3D HP models. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-polar lattice model. The simulation results show that the modified artificial bee colony algorithm can successfully be applied to the protein folding problem.

关键词： Protein folding Protein structure prediction HP-model Swarm intelligence artificial bee colony algorithm

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A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

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INFORMATION SCIENCES 2016年 367卷 1012-1044页

作者： Cui, Laizhong Li, Genghui Lin, Qiuzhen Du, Zhihua Gao, Weifeng Chen, Jianyong Lu, Nan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Xidian Univ Sch Math & Stat Xian Peoples R China

Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed. (C) 2016 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Depth-first search framework Computing resources allocation Novel search equations

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Multi-objective dynamic optimal power flow using improved artificial bee colony algorithm based on Pareto optimization

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第4期26卷 692-712页

作者： Liang, Ruey-Hsun Wu, Chang-Yo Chen, Yie-Tone Tseng, Wan-Tsun Natl Yunlin Univ Sci & Technol Dept Elect Engn Touliu 640 Yunlin Taiwan

This paper presents an improved artificial bee colony algorithm based on Pareto optimization for solving the multi-objective dynamic optimal power flow (DOPF) problem. To simulate the dynamic operational situations of a power system in 24h, various load demands related to each hour were considered in an OPF problem. The multi-objective DOPF problem was solved by minimizing total generation cost, total emission, and total real power loss while meeting some constraints. When considering a multi-objective optimization problem, one of the objective functions is improved, and the other objective functions deteriorate. These functions cannot be improved simultaneously because they contradict each other. The global Pareto optimal front, which is composed of a set of nondominated solutions, was identified in the multi-objective optimization problem. Power system operators can appropriately select one of the nondominated solutions according to various situations. An improved artificial bee colony algorithm was used as optimal tool on the optimization update procedure. To avoid yielding local optimal solutions to the problem, a skill called chaos queues was added. To demonstrate the effectiveness of the proposed method, the multi-objective DOPF was performed on the IEEE 30-bus and 118-bus systems. The results of using the proposed method were compared with the results of using other algorithms, revealing the effectiveness of the proposed method in the multi-objective DOPF problem. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： artificial bee colony algorithm dynamic optimal power flow Pareto optimization power system operation

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A NOVEL EXPRESSION IN CALCULATING RESONANT FREQUENCY OF H-SHAPED COMPACT MICROSTRIP ANTENNAS OBTAINED BY USING artificial bee colony algorithm

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2010年第14-15期24卷 2049-2061页

作者： Akdagli, A. Toktas, A. Mersin Univ Fac Engn Dept Elect & Elect Engn TR-33343 Ciftlikkoy Mersin Turkey Mersin Univ Dept Informat Technol TR-33343 Ciftlikkoy Mersin Turkey

In this paper, a new, simple and accurate expression for the resonant length in calculating resonant frequency of H-shaped compact microstrip antennas at UHF frequencies applications is presented. H-shaped compact microstrip antennas with a range of different physical dimensions built on various substrates are numerically simulated with a software package based on finite-difference time domain method. A closed-form expression for the resonant length is constructed with the aid of both the artificial bee colony algorithm and the simulation data. The resonant frequency is then calculated by means of this resonant length expression. The average percentage error is found to be less than 0.79% for 216 simulated antennas. Experimental and simulated results reported elsewhere have been employed to illustrate the validity and accuracy of the present formulation.

关键词： Plato's number percentage errors simulated data artificial bee colony algorithm resonant frequencies Software packages Microstrip antennas Compact

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An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期60卷 1149-1159页

作者： Han, Yu-Yan Pan, Quan-Ke Li, Jun-Qing Sang, Hong-yan Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252059 Peoples R China

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.

关键词： Blocking flowshop Makespan artificial bee colony algorithm Differential evolution

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Design of auto disturbance rejection controller for train traction control system based on artificial bee colony algorithm

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MEASUREMENT 2020年 160卷 107812-107812页

作者： Wang, Biao Yang, Jie Jiao, Haining Zhu, Kuan Chen, Yuqi Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou Peoples R China

This paper focuses on train running speed tracking control of electric traction freight train. The physical model of freight train is more complex and the parameters are more unstable than other trains. Therefore, the traditional PID controller is affected by the unmodeled dynamic and unknown parameter changes, resulting in large and unstable train speed tracking error. The design of NLADRC(Nonlinear Active Disturbance Rejection Controller, NLADRC) algorithm based on the time-delay control model of train in this paper. It reduces the dependence on the train model itself and has good tracking performance. The NLADRC gives full play to the strong function and high efficiency of the nonlinear function, while the introduction of the artificial bee colony algorithm with excellent experience effectively solves the problem that the parameters of the NLADRC algorithm are difficult to be adjusted. In the simulation, part of the mechanical delay of the system are regarded as internal and external disturbance, the results show that compared with the traditional PID algorithm, this algorithm has the advantages of high tracking accuracy, strong anti-interference ability and faster response speed. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nonlinear Active Disturbance Rejection Controller Freight train artificial bee colony algorithm Speed tracking

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A novel artificial bee colony algorithm for HVAC optimization problems

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HVAC&R RESEARCH 2013年第6期19卷 715-731页

作者： Zhang, Xin Fong, Kwong Fai Yuen, Shiu Yin City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Div Bldg Sci & Technol Kowloon Hong Kong Peoples R China

The strategy of simulation-optimization is popular in handling the design and energy management of centralized HVAC systems. An effective optimization algorithm is paramount to obtain the optimal design and operation of HVAC systems. Due to the multi-dimensional and highly constrained nature commonly found in HVAC problems, traditional optimization methods are generally not applicable. Instead, a metaheuristc algorithm, e.g., the genetic algorithm, is a good choice to achieve these purposes. In recent years, the paradigm of the artificial bee colony is emerging in the field of evolutionary computation. The artificial bee colony optimizes the problem through the foraging behavior of honey bees. This article presents a novel approach developed from this paradigm, called the one-position inheritance artificial bee colony algorithm, for handling HVAC system optimization. Through two HVAC example problems, it is found that the one-position inheritance artificial bee colony can more effectively and efficiently search the optimal solutions against the genetic algorithm and original artificial bee colony. This suggests that the one-position inheritance artificial bee colony is a good optimization algorithm to solve other HVAC engineering problems with a multi-dimensional, mixed discrete-continuous, and highly constrained search landscape.

关键词： bee colonies High Voltage Alternating Current Systems artificial bee colony algorithm heating ventilation and air conditioning Optimization algorithms bee Dosage Form Not Applicable Genetic algorithm (GA) Inheritance

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