查看更多>>摘要：To address the multimodal and multi-objective optimization problems, various evolutionary algorithms are developed in the literature. The aim of these algorithms is to find the best feasible solution set for considered objectives. However, approaches developed in literature are unable to determine well distributed Pareto sets and Pareto front in the decision and the objective space respectively. There exists a trade-off in convergence and diversity of Pareto optimal solutions. In this paper, we propose an enhanced multi-objective particle swarm optimization (EMOPSO) method which uses Levy flight to enhance exploration and expedite the search to obtain multiple global optima. In addition, we introduce parameter gamma that judiciously intertwines exploration and exploitation. Hence, the proposed variant EMOSPO provides diversity in the decision and objective space simultaneously. This method is also successful in maintaining multiple stable niches for multimodal solutions and forms well distributed Pareto set and Pareto front as compared to ten state-of-the-art algorithms. The EMOPSO is evaluated on 24 multimodal multi-objective problems of CEC 2020 based on four performance indicators and is analyzed on the basis of time complexity. Performance of EMOPSO and competitive algorithms is also evaluated on four real world engineering problems. The compared algorithms are ranked on basis of average ranking and Friedman test. Experimental results and analysis show the superior performance of EMOPSO in comparison to the competing state-of-the-art multi-objective algorithms. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：This study proposed a hybrid approach that integrates supervised and unsupervised learning to estimate the tunnel boring machine (TBM) performance in soft soil under limited geological information. By combining the shared nearest neighbor (SNN) algorithm and the density-based spatial clustering of applications with the noise (DBSCAN) method, an unsupervised learning approach, SNN-DBSCAN method, is performed to extract useful knowledge from the TBM logged data. The supervised random forest (RF) method is further combined with the SNN-DBSCAN method to predict the key TBM performance indicator. A realistic mass rapid transit (MRT) project in Singapore is adopted to examine the efficiency of the proposed methodology. The results from this case study indicate that: (1) The proposed SNN-DBSCAN method is suitable to perform data mining tasks on TBM logged data as the clustering result has an average of 85.03% similarity with site observation; (2) The knowledge extracted from the proposed approach could assist on soil identification as well as operational parameters determination; (3) Compared to the conventional RF method, the proposed approach achieves a high prediction accuracy with the coefficient of determination (R-2) increasing from 0.78 to 0.92. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The recent decade has arisen a significant issue in the energy sector, which is how to select proper sources of renewable energy as a sustainable substitution for conventional forms of fossil fuels. The way of solving this problem will meaningfully affect the environmental development and economic growth. To handle the issue, various scholars have concentrated on preferring the desirable energy source by employing the decision-making model based on the different fuzzy sets methods. Therefore, the aim of this paper is two folds. Firstly, various renewable resources potential are reviewed, and secondly, an assessment model is developed for prioritizing renewable options. Five major resources, hydropower, solar, wind, biomass, geothermal are considered. The present paper attempted to propose an integrated method on the basis of the Weighted Aggregated Sum Product Assessment (WASPAS) method in a way to provide an effective solution to decision-making problems on interval-valued Pythagorean fuzzy sets (IVPFSs). For the aim of calculating the criteria weights, the subjective weights offered by decision-makers were combined with objective weights achieved by means of the similarity measure method. This combination helped to attain more realistic weights. In the case of objective and subjective weights, new similarity measures and enhanced score functions were applied to IVPFSs. In addition, a renewable energy source selection problem is addressed in order to demonstrate the developed method is completely applicable to the real-world Multi-Criteria Decision-Making (MCDM) problems. This study also involves a sensitivity analysis using various weights of criteria as well as various values of the method's parameters in a way to approve the developed method stability. As revealed by the performed analysis, the integration of the subjective and objective weights improved the developed method stability with various weights of criteria. To reliably evaluate the performance of the method developed here, its results were compared with those of different methods formerly proposed in the literature. The evaluation results showed that the wind energy with a maximum assessment score degree (0.6259) using the proposed method was found the best option for selecting renewable energy sources over diverse criteria. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The worldwide outbreak of coronavirus disease 2019 (COVID-19) has triggered an unprecedented global health and economic crisis. Early and accurate forecasts of COVID-19 and evaluation of government interventions are crucial for governments to take appropriate interventions to contain the spread of COVID-19. In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. The proposed model is with an encoder-decoder architecture and employs long short-term memory (LSTM) for temporal feature extraction and multi-head attention for long-term dependency caption. The model simultaneously takes historical information, a priori known future information, and pseudo future information into consideration, where the pseudo future information is learned with the covariate forecasting network (CFN) and multi-task learning (MTL). In addition, we also propose the degraded teacher forcing (DTF) method to train the model efficiently. Compared with other models, the ITANet is more effective in the forecasting of COVID-19 new confirmed cases. The importance of government interventions against COVID-19 is further inferred by the Temporal Covariate Interpreter (TCI) of the model. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Because of global manufacturing, the foundry production workshop has shifted from single-factory production to multi-factory production. The distributed flexible job-shop scheduling problem is studied in this paper, and a distributed sand casting job-shop scheduling problem optimization model is established. To solve this model, this paper proposes a hybrid teaching-learning-based optimization (HTLBO) algorithm that involves a three-layer coding solution and a variety of strategies for population initialization. The HTLBO consists of the teacher learning phase, teaching phase, and learning phase. To improve the quality of teachers in the proposed algorithm, this paper sets the dynamic teacher group and adopts the tabu search based on the critical path and key blocks to increase the number of teachers in the dynamic teacher group and conduct the process of the teacher learning phase. In the teaching and learning phase, a variety of crossover operators for teaching and learning operations is designed to realize the process of teaching and learning. Finally, the experimental results of a real sand casting enterprise case indicate that the proposed algorithm performs better than the other six algorithms. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：With the continuous development of economic globalization and the increasing strengthening of human exchanges, the Water-Energy-Food (WEF) nexus evaluation has become a new research area. The purpose of this paper is to develop an inverse approach of graph model for conflict resolution (GMCR) to the conflict problems of WEF nexus evaluation in real life. Specifically, due to lack of information, some decision makers' (DMs') preferences over states may be incomplete fuzzy preference relations. Therefore, an algorithm is devised to amend the incomplete fuzzy preference relation to the complete fuzzy preference relation. Subsequently, a complete ordinal score vector is proposed to describe the preference ranking over different states based on the complete fuzzy preference relation. Moreover, in the framework of the inverse approach of GMCR, some mathematical models with the least constraint conditions are proposed to obtain all the required preference relations for opponent DM, which are required to make a given state be stable under four basic stability definitions. Finally, WEF nexus evaluation in Shandong province is illustrated to demonstrate the usefulness of the inverse approach of GMCR with the incomplete fuzzy preference relations. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：By decoupling virtualized network functions from the dedicated network equipment on which they run, Network Function Virtualization (NFV) has brought a flexible and economical way to support the complex communication demands of different applications. Virtualized Network Functions (VNFs) can be dispatched and deployed as instances of plain software on or near the communication paths of applications to establish Service Function Chains (SFCs), so as to provide special packet processing operations beyond simple packet forwarding. However, it is still a great challenge to dynamically place appropriate network functions at suitable locations so as to improve the efficiency of establishing SFCs and optimize network resource utilization. In this paper, the mechanism of dynamically deploying customized network functions via NFV is proposed. By predicting the future popularities of applications to switches, it adaptively places most of the appropriate network functions in corresponding forwarding equipment before they are massively requested. The serious latency and extra resource consumption caused by real-timely dispatching and deploying most of the requested network functions will be avoided. Then, the approach of Ant Colony Optimization (ACO) inspired multi-switch cooperative network function providing is devised. By cooperating multiple forwarding equipment on the packet transmission path, it makes full use of the already placed network functions to support packet processing operations in time with the cost and delay factors jointly considered. Simulation results show that the proposed mechanism has significant improvements in time overhead and resource utilization compared with the current state of the art. Specifically, our mechanism is capable of improving the service delay, the function utilization ratio, and the SFC adjustment efficiency by about 14%, 10% and 12% respectively, compared with corresponding work. (c) 2022 Elsevier B.V. All rights reserved.
de Melo, Simone AparecidaPereira, Robson Bruno DutraReis, Allexandre Fortes da SilvaLauro, Carlos Henrique...
22页
查看更多>>摘要：Manufacturing process modeling and optimization is a challenging task due to the numerous objectives to be considered in the optimization. Generally, the optimization of these processes requires many objective optimization methods to deal with four or more objective functions. However, the correlation structure of the outputs cannot be disregarded. In this work, it is proposed the unsupervised learning of the outputs together with multi-objective evolutionary optimization of the turning process of AISI 4340 steel considering three scenarios varying the tool nose radius. A central composite design varying the process parameters is used to conduct the experimental tests. After tests and measurements of quality and productivity outputs the p correlated observed outputs are firstly transformed in m unobserved latent variables through factor analysis using principal axis extraction method and varimax rotation, with m < p. Subsequently, the relation between the process parameters and the scores of latent variables is modeled through response surface methodology. Multi-objective evolutionary optimization methods are applied in the reduced and uncorrelated set of response models of the transformed outputs. The multi-objective algorithms are compared through hypervolume metric and the pseudo-weights approach is used to decision making. The proposed method can also be applied in other multi-response processes with correlated outputs. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：The Choquet integral is an aggregation operator widely used to deal with interacting criteria in multiple criteria decision analysis (MCDA). A practical problem regarding the use of the Choquet integral is the identification of its parameters, which are known as Choquet capacity. While capacity identification has received considerable attention in the context of ranking problems, it is difficult to find solutions tailored to sorting problems, which is the focus of this paper. We propose a novel method based on the Stochastic Acceptability Multicriteria Analysis (SMAA), by introducing the concept of feasible categorization scenarios. Moreover, two novel SMAA-based descriptive measures are proposed in order to provide valuable information for decision aiding, including the interpretation of the Choquet capacity and the possibility of exploiting the space of capacities by considering the decision makers' initial preferences. Another feature of our proposal is the ability of dealing with unbalanced threedimensional decision arrays. Numerical experiments with synthetic data were conducted to assess the proposed method. Furthermore, the applicability of our proposal is also attested to a case study related to the evaluation of research institutions. (C) 2022 Elsevier B.V. All rights reserved.
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Elsevier