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An improved artificial bee colony algorithm for the multi-objective cooperative disassembly sequence optimization problem considering carbon emissions and profit

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ENGINEERING OPTIMIZATION 2025年第3期57卷 649-670页

作者： Chen, Zhaofang Cheng, Hao Liu, Yongfeng Aljuaid, Mohammed Fujian Univ Technol Sch Management Fuzhou Peoples R China Minist Transport Transport Planning & Res Inst Beijing Peoples R China King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia

The disassembly and recycling of electronic waste are essential for realizing residual value, lowering carbon emissions and fostering sustainable development. This article addresses multi-objective cooperative disassembly planning and algorithm optimization for waste mobile phones. The model aims to reduce carbon emissions during the disassembly process and to increase profits. Moreover, an improved artificial bee colony (IABC) algorithm is introduced to address the model. The model is validated using Apple smartphones as a case study. The results indicate that cooperative disassembly can lower carbon emissions by 21.04% and increase profits by 40.04% more than sequential disassembly. The efficiency of the improved algorithm was assessed using Friedman and Nemenyi tests. Significant differences were found between the IABC and the other three algorithms, with significant P-values of P = 0.001, P = 0.006 and P = 0.024. It is demonstrated that the proposed model and improved algorithm exhibit reliability and superiority.

关键词： Multi-objective optimization cooperative disassembly carbon emissions improved artificial bee colony algorithm sustainable development

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Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm

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NANOTECHNOLOGY AND PRECISION ENGINEERING 2025年第2期8卷

作者： Yue, Chong Chen, Mantong Lang, Yaopu Liu, Qinggang Tianjin Univ State Key Lab Precis Measurement Technol & Instrum Tianjin 300072 Peoples R China Chongqing Acad Metrol & Qual Inspect Chongqing 401123 Peoples R China Tianjin Jinhang Tech Phys Inst Tianjin Key Lab Opt Thin Film Tianjin 300308 Peoples R China

This paper uses an innovative improved artificial bee colony (IABC) algorithm to aid in the fabrication of a highly responsive phase-modulation surface plasmon resonance (SPR) biosensor. In this biosensor's sensing structure, a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide (BlueP/TMDC) hybrid structure and graphene. In the optimization function of the IABC method, the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity. The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm. The results indicate that optimization using the IABC method gives significantly more phase sensitivity, together with lower reflectivity, than can be achieved with the traditional ABC method. The highest phase sensitivity of 3.662 x 10(6) degrees/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene. Moreover, analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules. Thus, given the exceptional sensitivity achieved, the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.

关键词： SPR Phase modulation Sensitivity improved artificial bee colony algorithm BlueP/TMDC Heterostructure

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improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2017年第2期25卷 218-238页

作者： Ding, Zhenghao Lu, Zhongrong Huang, Min Liu, Jike Sun Yat Sen Univ Sch Engn Guangzhou Guangdong Peoples R China

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee's behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.

关键词： Crack identification natural frequency optimization problem improved artificial bee colony algorithm beam structure

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Optimum outflow determination of the multi-reservoir system using constrained improved artificial bee colony algorithm

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SOFT COMPUTING 2020年第14期24卷 10739-10754页

作者： Moeini, Ramtin Soghrati, Farnaz Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

In this research, a new meta-heuristic algorithm, named artificial bee colony (ABC) algorithm, is used to solve multi-reservoir operation optimization problem. For this purpose, two improved versions of ABC are proposed by modifying the structure of original standard form of ABC algorithm. Furthermore, in order to increase the performance of proposed algorithms for solving large-scale problems, the constrained versions of original and improved form of ABC algorithms have been proposed in which the problem constraints are explicitly satisfied. Two benchmark text examples, including four- and ten-reservoir operation optimization problems, are solved here using proposed algorithms, and the results are presented and compared. In order to solve these problems, here, two formulations are also proposed in which in the first formulation, the water releases from the reservoir and in the second one the water storage volumes of the reservoir are considered as the decision variables of the problem. Comparison of the results shows that by using the improved ABC algorithm, the better results are obtained with less computational effort in comparison with the original form of ABC algorithm in which the result improvement is notable when the proposed constrained version of the algorithms is used.

关键词： improved artificial bee colony algorithm Optimal operation Multi-reservoir system Exploration Exploitation

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A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and improved artificial bee colony algorithm

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IEEE ACCESS 2019年 7卷 117612-117626页

作者： Gao, Jingpeng Lu, Yi Qi, Junwei Shen, Liangxi Harbin Engn Univ Coll Informat & Commun Harbin 150001 Heilongjiang Peoples R China

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signalto-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at -4 dB, 99.82% at 6 dB.

关键词： Radar signal recognition non-negative matrix factorization network transfer learning feature fusion improved artificial bee colony algorithm

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Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2501-2529页

作者： Yu, Zhenao Duan, Peng Meng, Leilei Han, Yuyan Ye, Fan Liaocheng Univ Sch Comp Sci Liaocheng 52059 Peoples R China

Effective path planning (PP) is the basis of autonomous navigation for mobile robots. Since the PP is an NP-hard problem, intelligent optimization algorithms have become a popular option to solve this problem. As a classic evolutionary algorithm, the artificial bee colony (ABC) algorithm has been applied to solve numerous realistic optimization problems. In this study, we propose an im-proved artificial bee colony algorithm (IMO-ABC) to deal with the multi-objective PP problem for a mobile robot. Path length and path safety were optimized as two objectives. Considering the com-plexity of the multi-objective PP problem, a well-environment model and a path encoding method are designed to make solutions feasible. In addition, a hybrid initialization strategy is applied to generate efficient feasible solutions. Subsequently, path-shortening and path-crossing operators are developed and embedded in the IMO-ABC algorithm. Meanwhile, a variable neighborhood local search strat-egy and a global search strategy, which could enhance exploitation and exploration, respectively, are proposed. Finally, representative maps including a real environment map are employed for simula-tion tests. The effectiveness of the proposed strategies is verified through numerous comparisons and statistical analyses. Simulation results show that the proposed IMO-ABC yields better solutions with respect to hypervolume and set coverage metrics for the later decision-maker.

关键词： path planning multi-objective optimization improved artificial bee colony algorithm hybrid initialization strategy variable neighborhood local search

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FIR digital filter design based on improved artificial bee colony algorithm

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SOFT COMPUTING 2022年第24期26卷 13489-13507页

作者： Lian, Lian Tian, Zhongda Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110146 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

The traditional swarm intelligence optimization algorithm is prone to fall into local optimal solutions in finite impulse response (FIR) digital filter design and has slow convergence speed. In order to optimize the design of FIR filter, a FIR digital filter design method based on improved bee colony (ABC) algorithm is proposed. This improved ABC algorithm can adaptively adjust the step size of the selected neighborhood of nectar source location. At the same time, the information of the global optimal solution is used to guide the search of candidate solution, which improves the global search ability of the algorithm. The improved ABC algorithm can balance the conflict between local search ability and global search ability, so it can achieve better optimization effect. The time and space complexity of the algorithm is analyzed in detail. Then, the improved ABC algorithm is used to design three typical FIR digital filters, namely low-pass, band-pass and band-stop filter. The performance of the designed filter is tested by simulation experiments. The experimental results show that compared with other state-of-the-art optimization algorithms, the proposed FIR filter design method has achieved better effect and performance. Meanwhile, the proposed design method has shorter optimization time. The superiority of the proposed method is verified.

关键词： Filter design FIR filter improved artificial bee colony algorithm Optimization

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Multi-robot multi-station welding flow shop closed-loop rescheduling with deep reinforcement learning and improved artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Zhang, Peng Wang, Ming Zhang, Guoqing Zheng, Peng Jin, Mengyu Zhang, Jie Donghua Univ Inst Artificial Intelligence Shanghai Engn Res Ctr Ind Big Data & Intelligent Shanghai Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai Peoples R China Donghua Univ Coll Mech Engn Shanghai Peoples R China Shanghai Maritime Univ Logist Engn Coll Shanghai Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

Multi-robot multi-station manufacturing system has gained significant popularity in welding workshop for processing large jobs. The implementation of cooperative welding robots effectively enhances the efficiency and flexibility of the system. However, robot failures also bring challenges for rescheduling to determine the rescheduling timing and re-assign appropriate robots for the affected processes so as to minimize the losses in performance and the maximum completion time. Distinct from open-loop rescheduling approach, this paper proposes a closed-loop rescheduling method via deep reinforcement learning and improved artificial bee colony algorithm. This method addresses two crucial questions: when to trigger a rescheduling, and how to provide a rescheduling scheme. For the first issue, current methods make decisions according to indicators derived from expert experience, failing to adapt to the dynamic environment. Therefore, we propose a deep reinforcement learning method to learn the adaptive trigger policy for the trigger agent under real-time running state. For the second issue, we present an improved artificial bee colony algorithm to re-assign robots without changing processing sequence and feed the performance back to trigger agent. Specially, a variable local search method is incorporated to avoid falling into local optimum. Finally, the proposed method is applied to solve the instances with different scales and a practical case. The experimental results of both demonstrate that the proposed closedloop rescheduling method can greatly reduce the deviation of the total completion time and the maximum completion time and have superior performance over other algorithms.

关键词： Multi -robot multi -station welding flow shop Robot failure Closed -loop rescheduling method Deep reinforcement learning improved artificial bee colony algorithm

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AN ADAPTIVE ONLINE SEQUENTIAL EXTREME LEARNING MACHINE FOR SHORT-TERM WIND SPEED PREDICTION BASED ON improved artificial bee colony algorithm

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NEURAL NETWORK WORLD 2018年第3期28卷 191-212页

作者： Tian, Z. Wang, G. Ren, Y. Li, S. Wang, Y. Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

关键词： extreme learning machine improved artificial bee colony algorithm adaptive online sequential short-term wind speed prediction

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Research on efficient-efficient routing protocol for WSNs based on improved artificial bee colony algorithm

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IET WIRELESS SENSOR SYSTEMS 2017年第1期7卷 15-20页

作者： Wang, Huadong Chen, Ying Dong, Shi Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Peoples R China Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Efficient and reliable routing protocol is one of the key technologies in wireless sensor networks, and it has been concerned by a lot of researchers. In light of the random selection of cluster head in low energy adaptive clustering hierarchy protocol algorithm, the residual energy of nodes, node location and node density are not considered. Through theoretical deduction and mathematical calculation, the study found that it is a NP - hard problem. A swarm intelligence algorithm was introduced into the clustering algorithm of wireless sensor network, and an efficient and reliable clustering algorithm for wireless sensor networks (WSNs) based on quantum artificial bee colony algorithm was proposed. Considering the residual energy of nodes, node location and node density, and the energy consumption of the network is balanced. Experiments show that the proposed routing protocol outperforms other work in most cases by reducing the network energy consumption for WSNs by more than 13%, which indicates the feasibility of the authors' approach for efficient and reliable routing protocol in wireless sensor networks.

关键词： energy conservation routing protocols wireless sensor networks pattern clustering computational complexity optimisation swarm intelligence energy consumption telecommunication power management telecommunication network reliability efficient-efficient routing protocol WSN improved artificial bee colony algorithm wireless sensor network random selection cluster head low energy adaptive clustering hierarchy protocol algorithm NP hard problem swarm intelligence algorithm energy consumption

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