Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Han, Xue; Han, Yuyan; Chen, Qingda; Li, Junqing; Sang, Hongshi; Liu, Yiping; Pan, Quan-Ke; Nojima, Yusuke

doi:10.23919/csms.2021.0018

Cited by 103 publications

(40 citation statements)

References 47 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”

Section: Discussionmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

View full text Add to dashboard Cite

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

show abstract

Section: Discussionmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

View full text Add to dashboard Cite

show abstract

“…MAs, inspired by nature, are algorithms that are based on population and probability [27][28][29][30][31][32][33][34][35][36][37]. A wide range of MAs have been used to many applications such as expensive optimization problems [38,39], multi-objective or many optimization problems [40][41][42][43], gate resource allocation [44,45], wind speed prediction [46], and scheduling problems [47]. They can also effectively solve unknown parameter of solar cells, including but not limited to, genetic algorithm (GA) [48], particle swarm optimizer (PSO) [49], differential evolution (DE) [50], grey wolf optimization (GWO) [51], Harris hawk optimizer (HHO) [12,52], slime mould algorithm (SMA) [53], cat swarm optimization (CSO) [54], sunflower optimization algorithm (SFO) [55], multi-verse optimizer (MVO) [56], demand response algorithm (DRA) [57], ant lion optimizer (ALO) [58], and firework algorithm (FWA) [59].…”

Section: Authors Methods Remarksmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

In this paper, an efficient sine cosine differential gradient‐based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient‐based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima; and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.

show abstract

“…According to the study, the performance of the classifier is affected greatly by its inner parameters and the features in the data. Metaheuristics have great effectiveness in solving this type of problem as shown in many works [28][29][30][31][32], such as object tracking [33,34], the traveling salesman problem [35], gate resource allocation [36,37], multi-attribute decisionmaking [38,39], the design of the power electronic circuit [40,41], fractional-order controllers [42], medical diagnoses [43,44], big data optimization problems [45], green supplier selections [46], economic emission dispatch problems [47], scheduling problems [48,49], and combination optimization problems [50]. This study proposes an enhanced crow search algorithm (CSA) [51] to simultaneously optimize the hyperparameters of the kernel extreme learning machine (KELM) and the feature space for predicting the entrepreneurial intention of college students.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

show abstract

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Cited by 103 publications

References 47 publications

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Contact Info

Product

Resources

About