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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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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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Optimal vegetable selection in urban and rural areas using artificial bee colony algorithm: Heavy metal assessment and health risk

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 139卷

作者： Gueltekin, Yucel Bayraktar, Mukaddes Kilic Sevik, Hakan Cetin, Mehmet Bayraktar, Tugrul Inst Nat Sci Grad Sch Nat & Appl Sci Dept Sustainable Agr & Nat Plant Resources TR-37150 Kastamonu Turkiye Karabuk Univ Fac Hlth Sci Dept Nutr & Dietet TR-78000 Karabuk Turkiye Kastamonu Univ Fac Engn & Architecture Dept Environm Engn TR-37150 Kastamonu Turkiye Ondokuz Mayis Univ Fac Architecture Dept City & Reg Planning TR-55420 Samsun Turkiye Karabuk Univ Fac Engn Dept Ind Engn TR-78000 Karabuk Turkiye Karabuk Univ Fac Hlth Sci Dept Nutr & Dietet TR-78050 Karabuk Turkiye

Industrial and traffic activities have raised heavy metal (HM) pollution, increasing health risks from contaminated vegetables. The study aims to analyze HM concentrations of lead (Pb), iron (Fe), and aluminum (Al) in Solanum lycopersicum L. (tomato), Capsicum annuum L. (pepper), Phaseolus vulgaris L. (bean), and Zea mays L. (corn) plants grown in urban and rural areas of Ordu province, T & uuml;rkiye. Variations in the HMs were evaluated based on species, organ, growing area, and washing status. The goal is to use the artificial bee colony (ABC) algorithm to identify the best vegetable combination based on health risk assessment. Tomato and corn had the lowest HM levels, while pepper had the highest. Urban vegetables had high Pb levels, with urban-grown corn showing notably high Fe and Al levels. Pb levels (341.4-13,240.4 mu g/kg) exceeded permissible limits in all vegetables, Al (898.9-210,706.2 mu g/kg) in most, while Fe (11.2-298.4 mu g/kg) stayed within safe limits. Health risk assessments (hazard quotient and hazard indices <1) show no risk of non-carcinogenic diseases. The recommended upper limits for HM concentrations constrain vegetable choices to minimize health risks, with the ABC algorithm advising washed pepper, tomato, and bean from urban areas and unwashed corn from rural areas.

关键词： Heavy metal accumulation Traffic density Vegetables Food safety Health risk assessment artificial bee colony algorithm

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Many-Objective artificial bee colony algorithm Based on Dual Indicators 5th

Many-Objective Artificial Bee Colony Algorithm Based on Dual...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Shaowei Xiao, Dong Liao, Futao Wang, Hui Hu, Min Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

ISBN: (纸本)9789819770038;9789819770045

artificial bee colony (ABC) is a well-known swarm intelligence algorithm that has been widely applied to many optimization problems. However, ABC shows some shortcomings in tackling many-objective optimization problems (MaOPs). In this paper, a novel many-objective ABC based on dual indictors (called DIMABC) is proposed to solve MaOPs. In DIMABC, a convergence indicator based on favorable weights is used to guide the search, mating selection and environmental selection. Based on the convergence indicator, good solutions are selected to construct an elite set, which is used to construct a new search strategy. In addition, the probability selection model is also improved based on the convergence indicator in the onlooker bee stage. Finally, a new diversity indicator and the convergence indicator are used in the environmental selection to make a balance between convergence and diversity. To validate the optimization capability of the proposed DIMABC, nine WFG benchmark problems with 3, 5, 8, and 15 objectives are utilized. Experimental results show that DIMABC obtains superior performance when compared with five other well-established algorithms.

关键词： artificial bee colony algorithm Swarm intelligence Many-objective optimization Environment selection

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Dual search mode artificial bee colony algorithm based on exploration-exploitation tradeoff and its application

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Kongzhi yu Juece/Control and Decision 2025年第2期40卷 507-516页

作者： Wang, Ying-Cong Li, Bo Sun, Jun-Wei Wang, Yan-Feng School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

The artificial bee colony algorithm has the advantages of a simple structure and easy implementation. However, due to its strong exploration ability and weak exploitation capability, it also suffers from drawbacks such as low solution accuracy and slow convergence speed. To solve these problems, this paper proposes a dual search mode artificial bee colony algorithm based on exploration-exploitation tradeoff, which includes three core modules: exploration-exploitation control, exploration-exploitation execution, and exploration-exploitation reinforcement. In the control stage, we design an exploration-exploitation indicator based on the population’s evolutionary dynamics, which initially emphasizes exploration and gradually shifts towards exploitation. In the execution stage, we design search equations guided by differential solution information, with a focus on the implementation points of exploration and exploitation. In the enhancement stage, the onlooker bees are instructed to select high-quality food sources for further search, using criteria such as diversity ranking and fitness ranking. Numerical experiments are conducted on traditional test functions and CEC2013 test functions. The results demonstrate that compared with eight high-level artificial bee colony algorithms proposed in recent years, the proposed algorithm exhibits strong competitiveness in terms of solution quality and convergence speed. Finally, the proposed algorithm is applied to optimize the risk prediction model for esophageal cancer, yielding satisfactory results. © 2025 Northeast University. All rights reserved.

关键词： artificial bee colony algorithm esophageal cancer prediction exploration-exploitation tradeoff function optimization

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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 based on Parzen window method

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APPLIED SOFT COMPUTING 2019年 74卷 679-692页

作者： Gao, Weifeng Wei, Zhifang Luo, Yuting Cao, Jin Xidian Univ Sch Math & Stat Xian 710126 Shaanxi Peoples R China Xidian Univ Sch Cyber & Engn Xian 710126 Shaanxi Peoples R China

artificial bee colony (ABC) algorithm, based on the metaphor in foraging behavior of honey fee swarm, has been repeatedly criticized for its poor convergence, due to its known exploration bias. In order to enhance the performance of ABC, the paper develops a novel approach (named ABCPW). First, three popular search strategies with different characteristics are employed to construct a strategy candidate pool for obtaining high quality candidate individuals. Next, to cut down on computational cost, the Parzen window method is applied to estimate these candidate individuals and then select one as the offspring. In addition, two different neighborhood mechanisms are adopted to balance the convergence and the population diversity. Finally, the performance of ABCPW is tested on a series of benchmark functions. The experimental results not only demonstrate the stability and convergence of ABCPW, but also show ABCPW outperforms several popular algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Parzen window method artificial bee colony algorithm Strategy candidate pool Neighborhood mechanism

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artificial bee colony algorithm with multi-strategy adaptation

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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 with multiple search strategies

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APPLIED MATHEMATICS AND COMPUTATION 2015年 271卷 269-287页

作者： Gao, Wei-feng Huang, Ling-ling Liu, San-yang Chan, Felix T. S. Dai, Cai Shan, Xian China Univ Petr Sch Sci Qingdao 266580 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Shannxi Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China

Considering that the solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation which results in slow convergence, this paper studies whether the performance of ABC can be improved by combining different search strategies, which have distinct advantages. Based on this consideration, we develop a novel ABC with multiple search strategies, named MuABC. MuABC uses three search strategies to constitute a strategy candidate pool. In order to further improve the performance of the algorithm, an adaptive selection mechanism is used to choose suitable search strategies to generate candidate solutions based on the previous search experience. In addition, a candidate solution is generated based on a Gaussian distribution to exploit the search ability. MuABC is tested on a set of 22 benchmark functions, and is compared with some other ABCs and several state-of-the-art algorithms. The comparison results show that the proposed algorithm offers the highest solution quality, the fastest global convergence, and the strongest robustness among all the contenders on almost all the cases. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms artificial bee colony algorithm Strategy candidate pool Gaussian distribution Search equation

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artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第10期13卷 1483-1491页

作者： Dong, Xueshi Lin, Qing Xu, Min Cai, Yongle Qingdao Univ Coll Comp Sci & Technol Qingdao Shandong Peoples R China Wuhan Univ Comp Sch Wuhan Hubei Peoples R China Chang Jiang Waterway Inst Planning & Design Wuhan Hubei Peoples R China

Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real-world planning problems such as multi-machine engineering system (MES). Population-based algorithms such as genetic algorithm (GA) and simulated annealing (SA) algorithm are only used to solve small- or medium-scale cases in which the city number is <2000. Furthermore, in term of the convergence speed and solution quality, their performances have further improvement space. Since many real-world problems can be modelled by large-scale CTSP, it is necessary to study better algorithms to solve large-scale CTSP. Since artificial bee colony algorithm (ABC) can show good performance in solving combination optimisation problems, and therefore this study applied improved ABC algorithms to solve large-scale CTSP. The modified ABC algorithms use generating neighbourhood solution (GNS) to improve ABC for this problem, two limitation and optimisation conditions are applied into GNS during the process of reinserting cities. The extensive experiments verify that the improved algorithms can demonstrate better solution quality than the compared algorithms for large-scale CTSP.

关键词： simulated annealing search problems travelling salesman problems genetic algorithms artificial bee colony algorithm large-scale CTSP artificial bee colony algorithm combination optimisation problems multiple travelling salesman problems real-world planning problems multimachine engineering system population-based algorithms ABC algorithms large scale coloured travelling salesman problem genetic algorithm simulated annealing algorithm generating neighbourhood solution

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