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A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 109卷 151-168页

作者： Ng, K. K. H. Lee, C. K. M. Zhang, S. Z. Wu, Kan Ho, William Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Div Syst & Engn Management 50 Nanyang AveN3-20251 Singapore Singapore Univ Melbourne Management & Mkt Level 10198 Berkeley St Melbourne Vic 3010 Australia

An Online Vehicle Routing Problem is a formation of Capacitated Vehicle Routing Problem with rerouting strategy to resolve the problem of inefficient vehicle routing caused by traffic congestion. A flexible delivery rerouting strategy is proposed, which aims at reducing the risk of late delivery. The method of terminating an exploration in a solution by the original ABC algorithm, when the solution is trapped in local optima, is to abandon the solution after specific tolerance limits are set. The phenomenon of local optimal traps will be repeated rapidly after a lengthy recursive process and will eventually result in a low quality solution, with a more complex combinatorial problem when the capability of the exploration is restricted by an inflexible termination criterion. Therefore, this paper proposes a novel scheme using a Multiple Colonies artificial bee colony algorithm. The designs of the outstanding bee selection for colony communication show it to be superior in exploitation. The performance of the proposed algorithm is examined through by Capacitated Vehicle Routing instances and a case study, and the results indicate the potential of using real time information for data-driven vehicle scheduling. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Online vehicle routing problem Swarm intelligence artificial bee colony algorithm Multiple colony strategy

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A Modified artificial bee colony algorithm Based on the Self-Learning Mechanism

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algorithmS 2018年第6期11卷 78-78页

作者： Pang, Bao Song, Yong Zhang, Chengjin Wang, Hongling Yang, Runtao Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong Univ Weihai Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-inspired optimization algorithms, but there still exist several insufficiencies due to the inefficient solution search equation (SSE), which does well in exploration but poorly in exploitation. To improve accuracy of the solutions, this paper proposes a modified ABC algorithm based on the self-learning mechanism (SLABC) with five SSEs as the candidate operator pool;among them, one is good at exploration and two of them are good at exploitation;another SSE intends to balance exploration and exploitation;moreover, the last SSE with Levy flight step-size which can generate smaller step-size with high frequency and bigger step-size occasionally not only can balance exploration and exploitation but also possesses the ability to escape from the local optimum. This paper proposes a simple self-learning mechanism, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments on a set of 9 benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. The experimental results illustrated that the SLABC algorithm achieves significant improvement compared with other competitive algorithms.

关键词： artificial bee colony algorithm swarm intelligence self-learning solution search equation

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Characteristic Analysis of artificial bee colony algorithm with Network-Structure

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第3期21卷 496-506页

作者： Imamura, Shunta Kaihara, Toshiya Fujii, Nobutada Kokuryo, Daisuke Kitamura, Akira Kobe Univ Grad Sch Syst Informat Nada Ku 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan Tottori Univ Grad Sch Engn Koyama Tottori 6808552 Japan

The artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bees, is one of the swarm intelligence systems. This algorithm can provide an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

关键词： artificial bee colony algorithm network-structure swarm intelligence high-dimensional problem

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Design of the PI-UPFC-POD and PSS Damping Controllers Using an artificial bee colony algorithm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2017年第6期28卷 762-773页

作者： Barone Martins, Luis Fabiano de Araujo, Percival Bueno Fortes, Elenilson de Vargas Macedo, Leonardo H. Parana Fed Inst Educ Sci & Technol Ave Doutor Tito S-N BR-86400000 Jacarezinho PR Brazil Sao Paulo State Univ Ave Brasil Sul 56 BR-15385000 Ilha Solteira SP Brazil Goias Fed Inst Educ Sci & Technol Rua Maria Vieira Cunha 775 BR-75804714 Jatai Go Brazil

This paper presents two variations of the artificial bee colony (ABC) algorithm, the classical and a modified version, called GBest, for the design of the proportional-integral and supplementary damping controllers: power system stabilizers and the unified power flow controller (UPFC)-power oscillation damping set. The objective is to insert additional damping to the low-frequency oscillation modes present in multimachine electrical power systems, to guarantee the small-signal stability of the system considering different loading conditions. A new current injection formulation for the UPFC is proposed and incorporated into the current sensitivity model used to represent the dynamical operation of the electric power system. Static and dynamical analysis were performed for the New England system to validate the proposed formulation and to evaluate the performance of the optimization algorithms. The results indicate that the modified version of the ABC algorithm has superior performance for this problem, providing robust solutions, that ensure the stability of the system even when small variations of the load are considered.

关键词： artificial bee colony algorithm Current sensitivity model Power oscillation damping Power system stabilizers Unified power flow controller

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Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

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SAUDI JOURNAL OF BIOLOGICAL SCIENCES 2017年第3期24卷 703-710页

作者： Ding, Man Chen, Hanning Lin, Na Jing, Shikai Liu, Fang Liang, Xiaodan Liu, Wei Hebei Univ Technol Sch Architecture & Art Design Tianjin 300130 Peoples R China Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300387 Peoples R China Beijing Shenzhou Aerosp Software Technol Co Ltd Beijing 110000 Peoples R China Jilin Normal Univ Coll Informat & Technol Siping 136000 Peoples R China

This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method. (C) 2017 The Authors. Production and hosting by Elsevier B.V.

关键词： artificial bee colony algorithm Life-cycle evolving model Optimal power flow Multi-objective optimization

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Application of artificial bee colony algorithm in Vehicle Routing Problem with Time Windows

Application of Artificial Bee Colony Algorithm in Vehicle Ro...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Cong Chen Kang Zhou Wuhan Polytechnic University Wuhan China

ISBN: (纸本)9781538660584;9781538660577

In order to improve the accuracy of the artificial bee colony algorithm (ABC) on vehicle routing problem with time window (VRPTW), This paper makes the following improvements to the ABC. Three kinds of neighborhood search methods are introduced. In the leader bee and follower bee search stage, the single search mode is changed into a three-way search method, which improves the optimization depth of the algorithm. Multiple neighborhood searches of new food sources generated by the scouter bee is conducted and proceeding to the next iteration has enhanced the survival of new food sources and increased the diversity of populations. The global optimal solution is recorded by setting and updating the bulletin board. Simulation experiments show that the improved discrete ABC algorithm has obvious advantages in solving large-scale VRPTW. Therefore, the improved discrete ABC algorithm has great potential and application value in solving VRPTW.

关键词： artificial bee colony algorithm Mathematical model Optimization Heuristic algorithms Vehicle routing Sociology Statistics

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Modified Naive Bayes algorithm for Network Intrusion Detection based on artificial bee colony algorithm

Modified Naive Bayes Algorithm for Network Intrusion Detecti...

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IEEE International Symposium on Wireless Systems (IDAACS-SWS)

作者： Juan Yang Zhiwei Ye Lingyu Yan Wei Gu Ruoxi Wang School of Computer Science Hubei University of Technology Wuhan China Wuhan FiberHome Technical Services Co. Ltd.

ISBN: (纸本)9781538675885

Intrusion detection algorithm based on machine learning is a research hotspot in network security detection. The diversity of network intrusion detection data sets is one of the major factors that affect the practical application of machine learning. Therefore, some swarm intelligence algorithms were utilized to optimize parameters of machine learning methods for feature selection or feature weight in network intrusion. In this paper, a modified Naive Bayes algorithm based on artificial bee colony algorithm (ABCWNB, in brief) is proposed. The proposed method is tested on two public data sets and NSL-KDD data sets. Experimental results show that compared to Naive Bayes classifier based on genetic algorithm (GAWNB), Naive Bayes classifier based on grey wolf optimizer (GWOWNB), Naive Bayes classifier based on water wave optimization (WWOWNB) and basic Naive Bayes classifier, the proposed method can effectively improve the network intrusion detection rate, which can well detect various types of network intrusion and greatly improve the security performance of the network.

关键词： artificial bee colony algorithm Bayes methods computer network security feature selection genetic algorithms learning (artificial intelligence) particle swarm optimisation pattern classification artificial bee colony algorithm Network security Bayesian classifier Data Security Feature extraction Particle swarm optimization Bayes methods Pattern recognition Genetic algorithm (GA) Dataset Bayes

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Real-time Dynamic Track Planning of Multi-UAV Formation Based on Improved artificial bee colony algorithm

Real-time Dynamic Track Planning of Multi-UAV Formation Base...

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第37届中国控制会议

作者： Guozun Tian Lan Zhang Xin Bai Bing Wang School of Automation Electronic Engineering University of Science & Technology Beijing

Aiming at the Quad-rotor UAV formation in the environment of dynamic threats, improved artificial bee colony（IABC） algorithm was suggested in the real-time track planning because of its fewer control parameters and faster convergence. The simulation results show that the track planning based on IABC algorithm is faster than that based on ant colony optimization（ACO） algorithm in avoiding threats, and the real-time dynamic track planning based on IABC is effective in scenarios of the fixed and unfixed multi-UAV formation faced with dynamic threats.

关键词： multi-UAV formation dynamic track planning artificial bee colony algorithm

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A Novel Hybrid Vortex Search and artificial bee colony algorithm for Numerical Optimization Problems

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Wuhan University Journal of Natural Sciences 2017年第4期22卷 295-306页

作者： WANG Zhaowei WU Guomin WAN Zhongping School of Mathematics and Statistics Wuhan University Department of Mathematics and Physics Beijing Institute of Petrochemical Technology

Though vortex search（VS） algorithm has good performance in solving global numerical optimization problems, it cannot fully search the whole space occasionally. Combining the vortex search algorithm and the artificial bee colony algorithm（ABC） which has good performance in exploration, we present a HVS（hybrid vortex search） algorithm to solve the numerical optimization problems. We first use the employed bees and onlooker bees of ABC algorithm to find a solution, and then adopt the VS algorithm to find the best solution. In the meantime, we cannot treat the best solution so far as the center of the algorithm all the time. The algorithm is tested by 50 benchmark functions. The numerical results show the HVS algorithm has superior performance over the ABC and the VS algorithms.

关键词： numerical optimization problems vortex searchalgorithm artificial bee colony algorithm hybrid algorithm

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Optimal Placement and Sizing of Distributed Generator in Distribution System Using artificial bee colony algorithm

Optimal Placement and Sizing of Distributed Generator in Dis...

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IEEE Global Conference on Wireless Computing and Networking (GCWCN)

作者： Rashmi Deshmukh Amol Kalage Department of Electrical Engineering SIT Lonavala (MS) India

ISBN: (纸本)9781538652022;9781538652015

This paper presents, the application of artificial bee colony (ABC) algorithm to find optimal location and size of Distributed Generator (DG) for minimizing the power distribution loss and enhance the voltage profile. In this context, the ABC optimization algorithm is applied to a standard 14-bus and 57- bus system. The obtained results using single and multiple DGs are presented.

关键词： artificial bee colony algorithm Optimization Power system stability Conferences Wireless communication Generators Standards

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