Linqing Wang

Bio: Linqing Wang is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Scheduling (production processes) & Fuzzy logic. The author has an hindex of 5, co-authored 23 publications receiving 104 citations.

Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization

Abstract: For multi-objective optimization problems, particle swarm optimization ( PSO ) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space ( the objective functions are computationally expensive ), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε -Pareto active learning ( ε -PAL ) method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value of where the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines ( MLSSVM ) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization ( MOPSO ) algorithms.

A Generalized Heterogeneous Type-2 Fuzzy Classifier and Its Industrial Application

Abstract: Recently, evolving fuzzy systems have been proved to be effective in dealing with real-time data streams. However, their fixed structures are not flexible enough to address the structural variations triggered by the changing operating conditions or system states in complex industrial environments. A novel generalized heterogeneous interval type-2 (IT2) fuzzy classifier, named as GHIT2Class, is proposed in this paper, which is built upon a multivariable IT2 fuzzy neural network. To fully reflect the industrial data characteristics of uncertainty, this paper proposes an approach of constructing the uncertainty footprint with ellipsoidal rotation. A rule pruning method based on error and incentive intensity dynamic adjustment mechanism is reported in the process of modeling, and a corresponding rule recall mechanism is designed to avoid rules of catastrophic forgetting. In addition, the simultaneous update of the upper and lower bounds of IT2 fuzzy consequent parameters is designed to relieve the computing overhead of the fuzzy systems. The performance of the proposed GHIT2Class is experimentally validated by a number of synthetic datasets and industry study cases by using state-of-the-art comparative classifiers, where the proposed approach outperforms the others in achieving the best tradeoff between accuracy and simplicity.

Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input

Abstract: Prediction intervals ( PIs ) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks ( KDBN ) , serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference. This study proposes a variational inference method for the KDBN for the purpose of fast inference, which avoids the time-consuming stochastic sampling. The proposed algorithm contains two stages. The first stage involves the inference of the missing inputs by using a local linearization based variational inference, and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices. To verify the effectiveness of the proposed method, a synthetic dataset and a practical dataset of generation flow of blast furnace gas ( BFG ) are employed with different ratios of missing inputs. The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Abstract: Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

AI-based modeling and data-driven evaluation for smart manufacturing processes

Abstract: Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things ( IIOT ) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

A multi-layered gravitational search algorithm for function optimization and real-world problems

Abstract: A gravitational search algorithm ( GSA ) uses gravitational force among individuals to evolve population. Though GSA is an effective population-based algorithm, it exhibits low search performance and premature convergence. To ameliorate these issues, this work proposes a multi-layered GSA called MLGSA. Inspired by the two-layered structure of GSA, four layers consisting of population, iteration-best, personal-best and global-best layers are constructed. Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population. Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low, medium and high dimensions demonstrates that MLGSA is the most competitive one. It is also compared with four particle swarm optimization variants to verify its excellent performance. Moreover, the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance. The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance. Its computational complexity is given to show its efficiency. Finally, it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization

Abstract: This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

Abstract: Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances.