A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

Thang, Ta Bao; Bình, Huỳnh Thị Thanh

doi:10.1016/j.knosys.2022.108225

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Topic 6- “Nature-Inspired Algorithms for Strategic Intelligence” represents novel problem-solving and optimization methodologies that are illumed by natural phenomena and processes. These methods include evolutionary algorithms (Biswas et al ., 2022), particle swarm optimization (Liu et al ., 2022a, b, c, d, e; Zhang et al ., 2022c), ant colony algorithm (Yu et al ., 2022), spider monkey optimization (Zhu et al ., 2022), white shark optimization (Braik et al ., 2022), firefly algorithm (Thang and Binh, 2022), artificial bee colony algorithm (Ye et al ., 2022) and many other. Nature-inspired algorithms are mostly used for solving complex business problems related to multi-dimensional and multi-modal resource optimization (Chen et al ., 2022c; Hou et al ., 2022).…”

Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…In the following four cases, the minimum envelope entropy of the bearing vibration signal is taken as the fitness function, and the IGWO, AHA, HHO, MFO and PSO are used to enhance the performance of VMD, the number of population is set to 30, and the maximum iteration is set to 25. According to the parameter settings in [5,45], the value ranges of the decomposition number is set as [1, 15], and the value ranges of the penalty factor is set as [15,5000]. Comparative analysis is carried out to verify the quality of the IGWO algorithm's optimization of VMD parameters.…”

Section: Bearing Fault Diagnosis Experiments and Results Analysismentioning

confidence: 99%

“…Meta-heuristic algorithms and their improved algorithms have been shown to offer a great deal of potential for exploring effective combinations of model parameters. Thang and Binh [15] proposed a hybrid multitasking algorithm, a multi-factor firefly algorithm, to solve the problem of clustering the minimum routing cost tree. Ahmed et al [16] proposed an improved balance optimizer that achieved better classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Jin

et al. 2023

Meas. Sci. Technol.

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Accurately identifying the health status of train running gear bearings is crucial to en-sure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effec-tively filter out modal components containing rich fault information than the compari-son methods.

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“…Rohaninejad et al [15] combined the FA with Tabu Search for solving job shop scheduling. The FA is generally applied to numerous types of combinatorial optimization problems such as engineering design [16,17], flow-shop scheduling problem [18], clustered minimum routing cost tree problem [19], capacitated vehicle routing problem [20], and parallel machine scheduling problem [21].…”

Section: Firefly Algorithmmentioning

confidence: 99%

Firefly Algorithm with Tabu Search to Solve the Vehicle Routing Problem with Minimized Fuel Emissions: Case Study of Canned Fruits Transport

Paisarnvirosrak,

Rungrueang

2023

LOGI – Scientific Journal on Transport and Logistics

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Nowadays, logistics is used to assess economic growth and address energy consumption and environmental problems. Optimizing collection and delivery routes reduces fuel consumption and cost, thereby minimizing greenhouse gas (GHG) emissions. The vehicle routing problem with time windows (VRPTW) is addressed to identify routes that minimize total transportation costs, fuel consumption, and GHG emissions based on collection and delivery activities. Firefly Algorithm (FA) integrated with Tabu Search (TS) as (FATS) was proposed within the case study concerning canned fruit transport. The results showed that the proposed method outperformed the existing approaches and reduced the fuel consumption from 31,286 to 26,314 litres per year. The proposed algorithm also reduced the number of used vehicles from seven to six, as five 6-wheel trucks and one 4-wheel truck, with transportation cost reduced from 1,061,851 to 893,108 Baht per year, as well as greenhouse gas emissions, which were reduced from 90,730 to 76,312 kg CO 2 per year.

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A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

Cited by 11 publications

References 38 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Firefly Algorithm with Tabu Search to Solve the Vehicle Routing Problem with Minimized Fuel Emissions: Case Study of Canned Fruits Transport

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