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Grouping and Reflection of the artificial bee colony algorithm for High-Dimensional Numerical Optimization Problems

IEEE ACCESS 2024年 12卷 91426-91446页

作者： Phoemphon, Songyut Suranaree Univ Technol Inst Digital Arts & Sci DIGITECH Nakhon Ratchasima 30000 Thailand

The artificial bee colony (ABC) algorithm, inspired by the cooperative foraging behaviors observed in bees, is a prominent example of a swarm intelligence algorithm that offers significant advantages in optimization problems. However, the efficacy of the ABC algorithm is limited in high-dimensional scenarios or when handling multimodal functions, which contain many local optima because of the random nature of the one-dimensional search process for improving the position in the employed and onlooker bee phases. As a result, ABC has a limited ability to obtain the optimum result (slow convergence rate). To address this limitation, this research introduces Grouping and Reflection of the artificial bee colony (GRABC), a distinctive adaptation of the traditional ABC algorithm meticulously tailored to meet the specific demands of high-dimensional numerical optimization problems by balancing exploration and exploitation processes. GRABC strategically incorporates vector reflection and inertial weighting to formulate equations vleft and vright, which enhance both the employed and onlooker bee phases, substantially improving the convergence speed and improving the exploitation process. Moreover, the integration of grouping bees facilitates the exploration of food sources by promoting diversification and improving the exploration process. Additionally, an equation is derived to accurately compute the new positions of scout bees (exploration process), accounting for the possibility of becoming stuck in local optima and considering the proper limit values. The effectiveness of GRABC is thoroughly evaluated using 32 numerical benchmark functions, mostly including CEC 2017, which encompasses 100 dimensions. The empirical findings compellingly demonstrate that the GRABC algorithm outperforms alternative methodologies in terms of solution quality and convergence characteristics, as substantiated by comprehensive assessments that include metrics such as the worst, best, and average

关键词： Optimization methods artificial bee colony algorithm Convergence Reflection Particle swarm optimization Vectors Search problems Optimization problem swarm intelligence artificial bee colony grouping vector reflection

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Crossover-based artificial bee colony algorithm for constrained optimization problems

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NEURAL COMPUTING & APPLICATIONS 2015年第7期26卷 1587-1601页

作者： Brajevic, Ivona Univ Belgrade Fac Math Belgrade 11000 Serbia

artificial bee colony (ABC) algorithm represents one of the most-studied swarm intelligence algorithms. Since the original ABC has been found to be very effective, today there are a lot of improved variants of ABC algorithm used to solve a wide range of hard optimization problems. This paper describes a novel artificial bee colony algorithm for constrained optimization problems. In the proposed algorithm, five modifications are introduced. Firstly, to improve the exploitation abilities of ABC, two different modified ABC search operators are used in employed and onlooker phases, and crossover operator is used in scout phase instead of random search. Secondly, modifications related to dynamic tolerance for handling equality constraints and improved boundary constraint-handling method are employed. The experimental results, obtained by testing on a set of 24 well-known benchmark functions and four widely used engineering design problems, show that the proposed approach can outperform ABC-based approaches for constrained optimization problems in terms of the quality of the results, robustness and convergence speed. Additionally, it provides better results in most cases compared with other state-of-the-art algorithms.

关键词： artificial bee colony algorithm Constrained optimization Nature-inspired algorithms Swarm intelligence Exploitation Exploration

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Learning high-order fuzzy cognitive maps via multimodal artificial bee colony algorithm and nearest-better clustering: Applications on multivariate time series prediction

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Li, Zhuofan Liu, Xiaoqian Zhang, Yingjun Qin, Jiahu Zheng, Wei Xing Wang, Jingping Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia Beijing Normal Univ Ctr Informat & Network Technol Beijing 100875 Peoples R China

As an effective soft computing method, fuzzy cognitive maps (FCMs) have been successfully utilized to process time series prediction problems. However, FCM-based time series prediction models face some challenges including the complicated spatial-temporal dependencies, the complex causal relations among different variables, the low convergence speed, the immersing local minimization, and the non-convex optimization problems. To address these challenges, we propose a multivariate time series prediction model combining niching-based artificial bee colony algorithm and high-order fuzzy cognitive maps (HFCMs), termed NABCHFCM. Firstly, the learning of the HFCM is divided into multiple multimodal optimization problems (MMOPs). Secondly, a complete mathematical frame via multimodal artificial bee colony algorithm and nearest-better clustering is established to solve all decomposed MMOPs. Finally, the learned HFCM can be employed to predict the time series evolution trend. Experimental results on eight multi-variate datasets have demonstrated better prediction and generalization performance of NABC-HFCM by comparison with several representative baseline algorithms as a whole.

关键词： Multivariate time series prediction High-order fuzzy cognitive maps artificial bee colony algorithm Multimodal optimization

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Improved artificial bee colony algorithm with dynamic population composition for optimization problems

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NONLINEAR DYNAMICS 2022年第1期107卷 743-760页

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Cent Lille CRIStAL UMR CNRS 9189 F-59651 Villeneuve Dascq France Beijing Jiaotong Univ Inst Syst Sci Beijing 100044 Peoples R China

The artificial bee colony (ABC) algorithm is an effective swarm-based meta-heuristic algorithm for optimization problems. Nevertheless, slow convergence speed has affected its competitiveness. In order to improve its performance, an improved ABC with dynamic composition (ABCDC) is proposed in this paper. Since the original ABC and its most variants use constant ratio between employed bees and onlooker bees, which causes that the number of onlooker bees is insufficient to exploit the searching space in limited time. Therefore, we propose a mechanism to adjust the number of employed bees and onlooker bees in order to find the global optimum more effectively. Moreover, Symmetric Latin Hypercube Design is utilized to enhance the diversity of initial population. Besides, two differential search equations with self-adaptive parameters are used in the employed bee phase and onlooker bee phase. Finally, to evaluate the performance of ABCDC, comparisons with four state-of-the-art ABC variations and the original one have been done on 22 benchmark problems with different dimensions. And four meta-heuristic algorithms were also involved to fully evaluate the effectiveness of ABCDC. The experimental results demonstrate that ABCDC is better than the competitors in terms of its solution quality and convergence speed.

关键词： artificial bee colony algorithm Dynamic population composition Parameter adaptation Solution search equation Symmetric Latin Hypercube Design

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A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities

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APPLIED MATHEMATICAL MODELLING 2014年第3期38卷 1111-1132页

作者： Li, Jun-Qing Pan, Quan-Ke Tasgetiren, M. Fatih Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Flexible job-shop scheduling problem with maintenance activities Multi-objective optimization artificial bee colony algorithm Tabu search

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Improved multi-objective artificial bee colony algorithm for parallel machine lot-streaming scheduling problem with limited and unequal sub-lots

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Zhu, Yingying Tang, Qiuhua Zhang, Liping He, Ming Kapenda, Jerry Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Inst Wuhan 430081 Peoples R China

Job splitting is a full-time effective measure used to improve corporate competitiveness, especially when the lot size of jobs is considerably large. This work investigates a parallel machine lot-streaming scheduling problem, where unequal sub-lot is considered and the number of sub-lots is strictly limited by that of cutters. Focus on this problem, a mixed-integer linear programming model is formulated to minimize makespan and due time deviation simultaneously, to scrutinize the coupling relationship among decision variables, i.e., the number and size of sub-lots, the machine allocation, and the processing order of all sub-lots. Then, an improved multi-objective artificial bee colony algorithm (IMOABC) with two categories of improvements is developed to obtain highquality Pareto front solutions. Specifically, a semi-random initialization is proposed to balance the workload as evenly as possible, which includes a half-normal distribution strategy for determining the number of utilized sub-lots, an equal-probability lot-splitting strategy for determining the size of each sub-lots, and a machine allocation strategy to synchronize the completion time of all machines. Besides, a multipoint preservative crossover is designed to enhance diversity in the employed bee phase, a sub-lot adjustment operator is proposed to implement objective-oriented local search in the onlooker bee phase, and an individual restart mechanism is adopted to avoid being trapped in local optimum in the scout bee phase. Extensive experiment results demonstrate that IMOABC significantly outperforms the other six state-of-the-art algorithms in terms of diversity and convergence.

关键词： Parallel machine lot-streaming scheduling Limited and unequal sub-lots Due time deviation artificial bee colony algorithm Semi-random initialization

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Optimal reactive power flow solution in multiterminal AC-DC systems based on artificial bee colony algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第5期22卷 1159-1176页

作者： Yalcin, Faruk Arifoglu, Ugur Sakarya Univ Dept Mechatron Engn Sakarya Turkey Sakarya Univ Dept Elect Elect Engn Sakarya Turkey

Active power loss optimization is one of the important goals in electrical power systems and it is provided by optimal reactive power flow (ORPF). In this study, a new approach for the solution of the ORPF in multiterminal AC-DC systems is proposed. This approach provides 3 main contributions. First, the convergence problem in the AC-DC power flow is solved. Second, the problem of getting stuck in local minima during the optimization process is overcome. Third, a better global optimum point is obtained for the ORPF. Active power loss optimization is implemented through the artificial bee colony (ABC) algorithm, which is a heuristic optimization method, by considering the system constraints. This study is the first to use the ABC algorithm for the solution of the ORPF in multiterminal AC-DC systems. The proposed approach is tested on the modified IEEE 14-bus AC-DC test system and the obtained results from this and other studies are given. Comparative results prove that this approach is more efficient and reliable in reaching a global optimum while satisfying all of the system constraints.

关键词： Optimal reactive power flow AC-DC artificial bee colony algorithm genetic algorithm

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Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第3期30卷 1195-1220页

作者： Saif, Ullah Guan, Zailin Zhang, Li Zhang, Fei Wang, Baoxi Mirza, Jahanzaib Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol HUST SANY Joint Lab Adv Mfg Wuhan 430074 Hubei Peoples R China Univ Engn & Technol Dept Ind Engn Taxila Pakistan

In multi-mixed model assembly lines, customer orders with different demand of models and due dates make it critical to decide the sequencing of different models and balancing of lines. Therefore, current research, first time, investigated an order oriented simultaneous sequencing and balancing problem of multi-mixed model assembly lines with an aim to minimize the variation in material usage, minimize the maximum makespan among the multi-lines and minimize the penalty cost of the late delivery models from different orders simultaneously. Moreover, a new mix-minimum part sequencing method is developed and a multi-objective artificial bee colony (MABC) algorithm is proposed to get the solution for the considered problem. Experiments are performed on standard assembly line data taken from operations library (OR) to test the performance of the proposed MABC algorithm against a famous multi-objective algorithm (Strength Pareto Evolutionary algorithm i.e. SPEA 2) in literature. Moreover, the proposed MABC algorithm is also tested on the data taken from a well reputed manufacturing company in China against the famous algorithm in literature (i.e. SPEA 2). End results indicate that the proposed MABC outperforms SPEA 2 algorithm for both standard data and company data problems.

关键词： Multi-mixed model assembly line Simultaneous sequencing and balancing Multi-objective optimization artificial bee colony algorithm Pareto solutions

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Time optimization of urban arterial coordinated control based on multidimensional neighbourhood artificial bee colony algorithm

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TRANSPORTATION PLANNING AND TECHNOLOGY 2024年第1期47卷 113-145页

作者： He, Ruichun Zhao, Hongxing Ye, Zhongni Yang, Liumeng Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Box 106 88 Anning West Rd Lanzhou Gansu Peoples R China

Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control.

关键词： Traffic arterial coordinated control signal timing optimization artificial bee colony algorithm

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Optimum cost design of RC columns using artificial bee colony algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第5期45卷 643-654页

作者： Ozturk, Hasan Tahsin Durmusa, Ahmet Karadeniz Tech Univ Fac Engn Civil Trabzon Turkey

Optimum cost design of columns subjected to axial force and uniaxial bending moment is presented in this paper. In the formulation of the optimum design problem, the height and width of the column, diameter and number of reinforcement bars are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the column consisting the cost of concrete, steel, and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

关键词： optimization metaheuristic artificial bee colony algorithm reinforced concrete columns

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