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artificial bee colony algorithm with multi-strategy adaptation

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第3期23卷 135-147页

作者： Guo, Zhaolu Li, Hongjin Zhang, Wensheng Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

To improve the convergence performance of artificial bee colony (ABC) algorithm for tackling some complex optimisation issues, a new ABC with multi-strategy adaptation (MSABC) is presented. A multi-strategy adaptation mechanism is implemented to boost the search performance in MSABC. In this mechanism, an evolution rate index is proposed to adaptively select strategies with different characteristics at various evolutionary stages. Meanwhile, for balancing exploration and exploitation, a novel search strategy oriented by the elite individual is applied in this mechanism. On the CEC2014 test set, MSABC is compared to other existing algorithms to assess its performance. As the results demonstrate, MSABC can obtain good convergence performance.

关键词： artificial bee colony algorithm multi-strategy adaptation evolution process elite orientation

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artificial bee colony algorithm-based design of discrete-time stable unknown input estimator

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INFORMATION SCIENCES 2023年第1期634卷 621-649页

作者： Satoh, Toshiyuki Nishizawa, Shun Nagase, Jun-ya Saito, Naoki Saga, Norihiko Akita Prefectural Univ Dept Intelligent Mechatron 84-4 Aza-Ebinokuchi Yurihonjo Akita 0150055 Japan Ryukoku Univ Fac Adv Sci & Technol 1-5 YokotaniSeta Oe Cho Otsu Shiga 5202194 Japan Kwansei Gakuin Univ Dept Engn Artificial Intelligence & Mech Engn Cour 1 Gakuen Uegahara Sanda Hyogo 6691330 Japan

This paper addresses the design problem of the discrete-time stable unknown input estimator (UIE) based on parameter optimization using the artificial bee colony (ABC) algorithm. First, a stability-guaranteed design method for UIEs is presented, and a sufficient condition for the applicability of the design is provided. Next, to design UIEs with good disturbance rejection properties, a new objective function is developed that incorporates both waveform-based and norm-based performance criteria to allow direct evaluation of the adverse effects of disturbances on system performance. Finally, the proposed design method is compared with the previous one using an objective function based on the estimated disturbance to confirm the improvement of the disturbance rejection properties at the plant output. Furthermore, as another approach to the design of stable UIEs, the original UIE design method is combined with a constrained ABC algorithm, and the approach is compared with the proposed one in terms of disturbance rejection properties.

关键词： Unknown input estimator Disturbance rejection artificial bee colony algorithm Parameter optimization

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artificial bee colony algorithm Based on Dimensional Memory Mechanism and Adaptive Elite Population for Training artificial Neural Networks

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IEEE ACCESS 2023年 11卷 107616-107637页

作者： Zhang, Yiyang Pang, Bao Song, Yong Xu, Qingyang Yuan, Xianfeng Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Levy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30- and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.

关键词： artificial bee colony algorithm Search problems adaptive elite population dimensional memory mechanism search equation artificial neural network

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artificial bee colony algorithm with an adaptive search manner and dimension perturbation

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NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 16239-16253页

作者： Ye, Tingyu Wang, Hui Wang, Wengjun Zeng, Tao Zhang, Luqi Huang, Zhikai Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Sch Business Adm Nanchang 330099 Jiangxi Peoples R China

artificial bee colony (ABC) can effectively solve some complex optimization problems. However, its convergence speed is slow and the exploitation capacity is insufficient at the last search stage. In order to tackle these issues, this paper proposes a modified ABC with an adaptive search manner and dimension perturbation (called ASDABC). There are two important search manners: exploration and exploitation. A suitable search manner is beneficial for the search. An explorative search strategy and another exploitative search strategy are selected to build a strategy pool. To adaptively choose an appropriate search manner, an evaluating indicator is designed to relate the current search status. According to the evaluating indicator, an adaptive method is used to determine which kind of search manner is suitable for the current search. Additionally, a dynamic dimension perturbation strategy is used to enhance the exploration and exploration ability. To verify the performance of ASDABC, 50 problems are tested including 22 classical functions and 28 complex functions. Experiment result shows that ASDABC achieves competitive performance when contrasted with seven different ABC variants.

关键词： artificial bee colony algorithm Adaptive search manner Evaluating indicator Dimension perturbation

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artificial bee colony algorithm with Dynamic Dimension Search 3

Artificial bee Colony Algorithm with Dynamic Dimension Searc...

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhao, Ming Wang, Meiyu School of Computer Science and Engineering Shenyang Jianzhu University Liaoning China

ISBN: (纸本)9798331506582

Aiming at the shortcomings of the standard artificial bee colony (ABC) algorithm due to weak exploitation capability and slow convergence speed caused by one-dimension search, an ABC algorithm with dynamic dimension search (DDSABC) is proposed. Firstly, a dynamic selection mechanism for dimension search strategy is designed, where one-dimension search is used in the early stage of search to exploit globally diverse individuals, and multi-dimension search is used in the late stage to explore convergent individuals;an elite moderately guided dimension selection mechanism is designed for multi-dimension search, where half of the dimensions are selected under the guidance of the elite mean to accelerate the evolution of potential dimensions, and the other half are selected by a randomized way to maintain a moderate randomness;in addition, suitable search equations are selected for the different characteristics of the two dimension search strategies to ensure the balance between exploration and exploitation capabilities. Finally, on 30 complex problems from the CEC2014 test set, ablation experiments and comparative experiments with the standard ABC and its 5 variants are analyzed, and the results show that the proposed algorithm in this paper performs better in terms of solution accuracy, stability and convergence speed. © 2024 IEEE.

关键词： artificial bee colony algorithm dimension selection dynamic dimension search strategy elite guidance multi-dimension search

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Low-carbon berth allocation: An analysis of the effectiveness of an enhanced multi-objective artificial bee colony algorithm based on a case study

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OCEAN & COASTAL MANAGEMENT 2025年 261卷

作者： Ma, Xiaomeng Pu, Xujin Jiangnan Univ Sch Business Wuxi 214122 Peoples R China

Marine terminals are essential components of international trade networks and global markets. To guarantee the rapid and consistent growth in maritime trade, managers must employ suitable techniques to handle operational challenges and meet market needs. One of the critical decisions in operational planning is the allocation of berths. A well-designed berth allocation plan can greatly boost the productivity and competitiveness of seaports. Despite the extensive research on berth allocation, there remains a notable gap in studies focusing on low-carbon berth allocation. As energy shortages and global warming intensify, low-carbon has increasingly become key terms across various sectors. Under the circumstances, this work addresses a multi-objective stochastic berth allocation problem for minimizing the average carbon emission and total service time. Firstly, a stochastic programming method is employed to formulate the uncertain arrival time and operation time of vessels, then a multi-objective chance-constrained programming model is constructed to formulate the studied problem. Secondly, an enhanced multi-objective artificial bee colony algorithm incorporating stochastic simulation (EMOABC) is specially designed. Finally, a large number of comparison experiments between EMOABC and nondominated sorting genetic algorithm II (NSGA-II) are performed. Through observing and analyzing the experimental results, two conclusions are acquired as follows: (i) EMOABC obtains the smaller IGD values and larger HV values than NSGA-II on all the test instances, indicating that it has better performance than NSGA-II for solving the considered problem;and (ii) EMOABC uses less running time in dealing with test problems of different scales compared to NSGA-II, suggesting that it has lower computational complexity than NSGA-II.

关键词： Berth allocation Carbon emission Multi-objective optimization artificial bee colony algorithm Marine pollution

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Multi-Objective Home Health Care Routing and Scheduling With Sharing Service via a Problem-Specific Knowledge-Based artificial bee colony algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1706-1719页

作者： Fu, YaPing Ma, XiaoMeng Gao, KaiZhou Li, ZhiWu Dong, HongYu Qingdao Univ Sch Business Qingdao 266071 Peoples R China Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China Beijing Technol & Business Univ Natl Engn Res Ctr forAgri Prod Qual Traceabil Beijing 100048 Peoples R China Beijing Technol & Business Univ Res Ctr Consumpt Big Data & Intelligent Decis Maki Beijing 100048 Peoples R China

Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare cost and improving service satisfaction. This work addresses a service resource routing and scheduling problem with sharing strategy among multiple HHC centers for given customers. Two objective functions are involved: minimizing the total operation cost including the fixed usage cost of centers, caregiver usage cost and service cost, and minimizing the total tardiness caused by delay service. Firstly, a mixed integer programming model is formulated to describe the concerned problem. Secondly, a multi-objective artificial bee colony algorithm with problem-specific knowledge (MABC-PK) is proposed. Three problem-specific knowledge-based heuristics are designed to initialize population. A crossover operation and a self-learning neighborhood selection method are developed to prompt collaborative search of population and external archive. Furthermore, two knowledge-based local search methods are proposed for refining solutions in the external archive via employing some observations and priority properties derived from the problem characteristics. Finally, extensive experiments are conducted by comparing the proposed approach with four widely-acknowledged multi-objective optimization methods and a mathematical programming solver CPLEX. The comparative results and statistical analysis confirm the strong competitiveness of MABC-PK for solving the concerned problem.

关键词： Home health care scheduling multi-objective optimization artificial bee colony algorithm

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Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

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ELECTRICAL ENGINEERING 2024年第1期106卷 567-579页

作者： Wang, Liming Liu, Yingming Pang, Xinfu Wang, Qimin Wang, Xiaodong Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China Shenyang Inst Engn Coll Informat Shenyang 110136 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Shenyang Inst Engn Coll Energy & Power Shenyang 110136 Peoples R China

A multiobjective environmental economic dispatching model of power system is established with minimum economic cost and pollution emission as the optimization objectives to meet the challenges of power system dispatching caused by the global energy crisis and climate warming and promote the realization of the double carbon goal. A multiobjective artificial bee colony algorithm (MOABC) based on nondominant sorting and improved greedy criterion is designed according to the characteristics of the model. In the design of the algorithm, the Taguchi method is used to optimize the parameters, the heuristic method is used to process the constraints dynamically, and a variety of comprehensive evaluation indexes are used to evaluate the algorithm's performance. In the simulation analysis, a six-machine power system and a ten-machine power system are simulated and compared with different algorithms to verify the proposed model's rationality and effectiveness. Finally, the technique for order preference by similarity to the ideal solution (TOPSIS) method is used to determine the optimal compromise solution to provide a reference for the scientific decision-making of dispatchers. The simulation results show that the MOABC algorithm achieved the lowest economic cost (161.159 x 10(3) yuan for the 6-machine system and 2.771 x 10(4) $ for the 10-machine system) and pollution emission (194.202 kg for the 6-machine system and 3.639 x 10(3) kg for the 10-machine system) compared to the multiobjective wind driven optimization, multiobjective particle swarm optimization, and NSGA-II algorithms.

关键词： artificial bee colony algorithm Environmental economic dispatch Multiobjective optimization Power system dispatch

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A hybrid artificial bee colony algorithm with high robustness for the multiple traveling salesman problem with multiple depots

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 260卷

作者： Tong, Minghao Peng, Zhenhua Wang, Qin Nanchang Univ Sch Math & Comp Sci Nanchang 330031 Peoples R China Wuhan Technol & Business Univ Wuhan 430065 Peoples R China

A hybrid artificial bee colony algorithm (AC-ABC) with high robustness is proposed to solve the multiple traveling salesman problem (MTSP) with multiple depots. It initially conducts small-scale local searches to generate a high-quality population. Subsequently, a probabilistic model is established to balance global and local searches in the process of updating this population and exploring the optimal solution for the MTSP based on pheromone concentration and city visibility. In the process of population representation and updating, we introduce a novel tensor representation, which not only offers more opportunities for crossover between populations, but also adaptively provides more route choices to meet the personalized needs of salesmen. Besides artificial bee colony (ABC), AC-ABC takes at least 23% less execution time than other algorithms to solve the MTSP on multiple TSPLIB instances, especially takes about 32%-93% less execution time than the ant colony-partheno genetic algorithm (AC-PGA). The travel cost of the optimal route obtained by AC-ABC is significantly better than that of ABC, partheno genetic algorithm (PGA), improved PGA (IPGA), and two-part wolf pack search (TWPS). AC-ABC always obtains less travel cost than AC-PGA when the number of cities n <= 150. AC-ABC only obtains about 0.5%-7.4% more travel cost than AC-PGA when the number of cities n> 150.

关键词： artificial bee colony algorithm Ant colony algorithm Multiple traveling salesmen problem Robustness Hybrid algorithm

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Improved artificial bee colony algorithm for pressure source parameter inversion of Sakurajima volcano from InSAR data

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Geodesy and Geodynamics 2024年第6期15卷 635-641页

作者： Leyang Wang Linghui Xie Can Xi Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources East China University of TechnologyNanchang 330013China School of Surveying and Geoinformation Engineering East China University of TechnologyNanchang 330013China

A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9,2020,to invert the magma source characteristics below the volcano based on the point source Mogi *** that the Sakurajima volcano is surrounded by sea,all the deformation data are used to obtain the location and magma eruption volume of the *** response to the weak local search capability of the artificial swarm algorithm,the difference between the global optimal individual and the un-roulette screened individual is introduced as the variance component in the onlooker *** simulation experiments verify the improvement of the algorithm in terms of convergence *** real experiments,the Sakurajima volcano inversion shows closer fitting results and smaller residuals compared to the existing ***,the convergence speed of the algorithm echoes with the simulation experiments.

关键词： Sakurajima volcano D-InSAR Mogi model artificial bee colony algorithm

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