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Au octahedrons monolayer film SERS substrate coupled with a hybrid metaheuristic algorithm-optimized ELM model: An analytical strategy for rapid and label-free detection of zearalenone in corn oil

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FOOD CHEMISTRY 2025年 476卷 143516页

作者： Zhu, Jiaji Qian, Hao Zhu, Afang Guo, Zhiming Chen, Quansheng Xu, Yi Yancheng Inst Technol Sch Elect Engn Yancheng 224051 Peoples R China Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China

This study developed a rapid, label-free analytical strategy for quantifying zearalenone (ZEN) in corn oil. A highly sensitive Au octahedrons (Ohs) monolayer film was synthesized as the surface-enhanced Raman spectroscopy (SERS) substrate. A hybrid metaheuristic algorithm that combines the particle swarm optimization (PSO) algorithm and the grey wolf optimizer (GWO) algorithms, was used to optimize an extreme learning machine (ELM) model (i.e., the PSOGWO-ELM model). The PSOGWO-ELM model analyzed the collected SERS spectra to determine ZEN contents in corn oil. The results demonstrated that the analytical strategy possessed excellent performance: the root mean squared error of the prediction set (RMSEP) = 0.2297 mu g/mL, the coefficient of determination of the prediction set (R2P) = 0.9907, and the ratio of performance to deviation of the prediction set (RPDP) = 10.3695. The proposed analytical approach shows considerable promise for the rapid, label-free, and accurate detection of trace levels of ZEN in corn oil.

关键词： Corn oil hybrid metaheuristic algorithm Surface-enhanced Raman spectroscopy Extreme learning machine Zearalenone

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An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 2663-2680页

作者： Song Ronghui Ni Liangrong Zhejiang Tongji Vocat Coll Sci & Technol Hangzhou 311231 Zhejiang Peoples R China China United Engn Corp Ltd Hangzhou 310052 Peoples R China

Optimization is one of the oldest sciences or practices. Since the beginning of mankind, people strived for perfection when it came to their creations, products, gains, or self-improvement. Extension of activities and their cost, time, and resource limitations have caused researchers to pay their attention to optimizing the activities in construction management engineering. Rapid development in optimization techniques to solve related problems in structural design can be achieved accordingly. In this paper, meta-heuristic algorithms used for strength, energy, and cost optimization of building material in construction management. The novel meta-heuristic algorithm can be used for electricity cost and peak load alleviation with the minimum user waiting time. The proposed model is implemented in a smart building in terms of electricity cost estimation for both a single smart home and a smart building. The results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of strength, energy, and cost optimization of building material in construction management engineering. This study has used the artificial intelligence (AI) model as particle swarm optimization (PSO) model to calculate the accurate and material-specific energy of three commonly used building materials as fly ash, copper slag, and phospo-gypsum. Two regression models as root mean square (RMSE) and coefficient of determination (R-2) were used to calculate the results. Following the results of (R-2) and RMSE, PSO has shown its higher performance in predicting the strength, energy, and cost of building materials besides revealing a significant and positive correlation among them.

关键词： hybrid metaheuristic algorithm Particle swarm optimization Cost optimization Construction management Artificial intelligence Building material

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A hybrid metaheuristic algorithm FOR THE MULTI-OBJECTIVE LOCATION-ROUTING PROBLEM IN THE EARLY POST-DISASTER STAGE

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第6期19卷 4663-4691页

作者： Yan, Tongren Lu, Fuqiang Wang, Suxin Wang, Leizhen Bi, Hualing Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Yanshan Univ Sch Econ & Management Qinhuangdao 066004 Hebei Peoples R China

Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by eval-uating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Opti-mization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are intro-duced to enhance its execution efficiency. Finally, the effectiveness and perfor-mance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid meta-heuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.

关键词： Emergency logistics location-routing fairness multi-objective hybrid metaheuristic algorithm

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A hybrid metaheuristic algorithm for generalized vertex cover problem

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MEMETIC COMPUTING 2018年第2期10卷 165-176页

作者： Hu, Shuli Li, Ruizhi Zhao, Peng Yin, Minghao Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130024 Jilin Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun 130024 Jilin Peoples R China

The generalized vertex cover problem (GVCP) extends classic vertex cover problems to take both vertex and edge weights into consideration in the objective function. The GVCP consists in finding a vertex subset such that the sum of vertex weights together with all the corresponding edge weights is minimized. In this paper, we proposed a hybrid metaheuristic algorithm to solve GVCP (MAGVCP for short) that is based on evolutionary search and iterated neighborhood search. The algorithm uses population initializing procedure to produce high quality solutions, applies a dedicated crossover to generate offspring solutions, and finally utilizes an iterated best chosen neighborhood search to find better solutions. Experiments carried on random instances and DIMACS instances demonstrate the effectiveness of the proposed algorithm.

关键词： hybrid metaheuristic algorithm Generalized vertex cover problem Neighborhood search Crossover

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Solving the inverse graph model for conflict resolution using a hybrid metaheuristic algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 806-819页

作者： Huang, Yuming Ge, Bingfeng Hipel, Keith W. Fang, Liping Zhao, Bin Yang, Kewei Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Ctr Int Governance Innovat Waterloo ON N2L 6C2 Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada

The paper is concerned with one of the most important questions in conflict analysis: How decision mak-ers can strategically interact in a conflict to reach a specified equilibrium? More specifically, an inverse preference optimization model is formulated to solve the problem of adjusting the preferences of deci-sion makers so as to make a specified state an equilibrium. To this end we define an algorithm which incorporates the hybridization of particle swarm optimization and genetic algorithm. The proposed al-gorithm adopts a random neighbor strategy for population initialization, the elite reservation and mixed selection operation, and a diversity strategy for population update to improve its efficiency and effec-tiveness when searching for the required preferences. This approach can help decision makers or third parties to focus their resources on guiding them toward preferences that lead to a specified resolution. Finally, a real-world dispute over exporting bulk water from Eastern Canada is used to demonstrate the applicability and effectiveness of the approach.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Group decisions and negotiations Graph model for conflict resolution (GMCR) hybrid metaheuristic algorithm Inverse analysis

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A hybrid metaheuristic algorithm to achieve sustainable production: involving employee characteristics in the job-shop matching problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2023年第4期40卷 246-270页

作者： Quan, Bingtao Li, Sujian Wu, Kuo-Jui Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Hainan Univ Management Sch Haikou Peoples R China Hainan Univ Management Sch Haikou 570288 Peoples R China

Sustainable production is proposed to guide manufacturers in achieving sustainable development goals. Although previous studies have developed diverse metaheuristics algorithms in finding the optimal method for balancing economic and environmental aspects, the social aspect is still omitted. In addressing this shortcoming, this study attempts to design a hybrid metaheuristic algorithm to balance economic performance with social expectations by matching the employees' characteristics with those of a job shop. This study contributes to (1) confirming the importance of considering the social aspect for strengthening the theoretical basis;(2) proposing a hybrid metaheuristic algorithm in matching employees' characteristics with the job shop;and (3) utilizing the optimal scenario leads the related firms in practicing sustainable production. The findings indicate that matching the employee-job-shop can fulfill the social expectation for generating positive effects on economic performance. If manufacturers insist on lowering labor costs, it may have a negative impact on production efficiency.

关键词： hybrid metaheuristic algorithm sustainable production employee's characteristics job shop employee-job-shop matching

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Determining consignment inventory strategy for a vendor and multiple buyers using a hybrid metaheuristic algorithm

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PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL 2013年第1期1卷 65-78页

作者： Molamohamadi, Zohreh Sharifyazdi, Mehdi Arshizadeh, Rahman Jafari, Azizollah Ismail, Napsiah Univ Putra Malaysia Dept Mech & Mfg Engn Sri Kembangan Selangor Malaysia Univ Sci & Culture Dept Ind Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran

This paper considers a centralized supply chain with consignment inventory (CI) system, where a vendor keeps a finished product and supplies it at the same price to multiple buyers. CI is a process where the buyer pays with delay for the goods supplied by the vendor. Considering the time value of money, this study compares two types of delay in payment in CI, which have been rarely considered (real use and order to order). Furthermore, two different metaheuristic algorithms, genetic algorithm (GA) and a hybrid algorithm, containing GA and particle swarm optimization algorithm, are used to maximize the supply chain net profit and calculate the proper values. Finally, a sensitivity analysis is performed to examine the effects of each parameter on the total net profit. Moreover, by applying a paired t-test, a comparison is made between two types of CI system and also between GA and the hybrid algorithm.

关键词： inventory nonlinear programming delay in payment hybrid metaheuristic algorithm discounted cash flow approach (DCF)

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Parameters estimation of double-cage induction motors using a hybrid metaheuristic algorithm 21

Parameters estimation of double-cage induction motors using ...

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21st International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Vukasinovic, Jovan Milovanovic, Milos Arsic, Nebojsa Radosavljevic, Jordan Statkic, Sasa Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia

ISBN: (纸本)9781665437783

In this paper, a hybrid metaheuristic algorithm, named the hybrid Phasor Particle Swarm Optimization and Gravitational Search algorithm (PPSOGSA), is proposed for estimating parameters of double-cage induction motors. The parameters are obtained by minimizing the objective function related to the error between the calculated and manufacturer data. The performances of the proposed algorithm are analyzed and evaluated using the motors of different powers. Compared to the original PSOGSA and other algorithms applied in solving the parameter estimation problem, it is found that the proposed algorithm has better performances.

关键词： induction motors parameter estimation hybrid metaheuristic algorithm Phasor Particle Swarm Optimization Gravitational Search algorithm

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Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 1865-1883页

作者： Tran-Ngoc, H. Khatir, S. Le-Xuan, T. De Roeck, G. Bui-Tien, T. Wahab, M. Abdel Univ Ghent Fac Engn & Architecture Dept Elect Energy Met Mech Construct & Syst B-9000 Ghent Belgium Univ Transport & Commun Fac Civil Engn Dept Bridge & Tunnel Engn Hanoi Vietnam Ho Chi Minh City Open Univ Fac Civil Engn Ho Chi Minh City Vietnam Katholieke Univ Leuven Dept Civil Engn Kasteelpk Arenberg 40 B-3001 Leuven Belgium Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened to traffic in 1929. Since the bridge has come into operation for a long time, structural health monitoring (SHM) is strictly necessary to guarantee safety and avoid serious incidents. This paper proposes a novel approach to model updating for the Guadalquivir bridge based on the vibration measurements combined with a hybrid metaheuristic search algorithm. Cuckoo Search (CS) is an evolutionary algorithm derived from global search techniques to look for the best solution. Nevertheless, CS contains some fundamental defects that may reduce its effectiveness in dealing with optimization issues. A main drawback of CS arises in the low convergence level because CS applies fixed values for parameters when looking for the optimal solution. In addition, CS relies a lot on the quality of original populations and does not have the capability to enhance the quality of the next generations. If the position of the original particles is far from the optimal places, it may be challenging to look for the best solution. To remedy the shortcomings of CS, we propose a hybrid metaheuristic algorithm (HGAICS) employing the advantages of both Genetic algorithm (GA) and Improved Cuckoo Search (ICS) to solve optimization problems. HGAICS contains two outstanding characteristics as follows: (1) GA is employed to create original particles with the best quality based on the capacity of crossover and mutation operators and (2) those particles are then applied to look for the global best derived from the flexible and global search ability of ICS. This paper also presents the application of wireless triaxial sensors (WTSs) taking the place of classical wired systems (CWSs) to the measurements. The use of WTSs increases dramatically the freedom in setting up experimental measurements. The results show that the performance of the proposed hybrid algorithm not only determines uncertain parameters of the Guad

关键词： Improved cuckoo search Large-scale railway bridge Finite element method Model updating Genetic algorithm Ambient vibration test hybrid metaheuristic algorithm Wireless triaxial transducers

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A novel hybrid improved dingo algorithm for unmanned aerial vehicle path planning

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2025年第1期47卷 1-15页

作者： Wang, Shoubin Lv, Xuanman Li, Youbing Jing, Lewei Fang, Xinchang Peng, Guili Zhou, Yuan Sun, Wenhao Tianjin Chengjian Univ Sch Control & Mech Tianjin 300384 Peoples R China STECOL Corp Power Construct Corp China Tianjin 300384 Peoples R China Harbin Inst Technol Sch Instrument Sci & Engn Harbin 150001 Peoples R China

The unmanned aerial vehicle (UAV) trajectory planning is a crucial research area research area in the field of unmanned aerial system (UAS), which aims to find optimal flight paths in complex and changing environments. It could ensure better performance of (unmanned aerial vehicles) UAVs during their missions. A new hybrid algorithm named HIDOA-SOS is proposed by combining improved dingo algorithm (HIDOA) and the symbiotic organism search algorithm (SOS) in this paper. The DOA algorithm is optimized with an update strategy to speed up convergence and maintain better population exploration. The optimized DOA algorithm is gradually merged with the SOS algorithm in order to improve the population development of the algorithm. The generated trajectory is smoothed by cubic B-spline curves to make the path more suitable for UAV flight. The results of simulation experiments show that the HIDOA-SOS algorithm can successfully obtain feasible paths and outperforms the DOA algorithm, SOS algorithm and SSA algorithm.

关键词： Unmanned aerial vehicles (UAVs) hybrid metaheuristic algorithm HIDOA-SOS algorithm Path planning

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