Wenli Du

Synchronization analysis of heterogeneous dynamical networks

This paper investigates the synchronization problem over heterogeneous dynamical networks, in which nonidentical dynamical systems coexist. By introducing a virtual target, synchronization with an error level, called quasi-synchronization, is analyzed in asymmetric and symmetric networks, respectively. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived to estimate the synchronization error. Finally, two networks with six different Chuas circuits are provided to verify the theoretical results.

Dynamic Optimization of Industrial Processes With Nonuniform Discretization-Based Control Vector Parameterization

This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.

Biogeography-based learning particle swarm optimization

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

Monitoring for Nonlinear Multiple Modes Process Based on LL-SVDD-MRDA

This study proposes an online monitoring technique for nonlinear multiple-mode problems in industrial processes. The contributions of the proposed technique are summarized as follows: 1) Lazy learning (LL), a new adaptive local modeling method, is introduced for multiple-mode process monitoring. In this method, multiple modes are separated and accurately modeled online, and the between-mode dynamic process is considered. 2) The modified receptor density algorithm (MRDA) exhibiting superior nonlinear ability is introduced to analyze the residuals between the actual system output and the model-predicted output. The simulation of the Tennessee Eastman process with multiple operation modes shows that compared with other techniques mentioned in this study, the proposed technique performs more accurately and is more suitable for nonlinear processes with multiple operation modes.

Biogeography-based optimization with covariance matrix based migration

Display Omitted Covariance matrix-based migration (CMM) is proposed.CMM significantly enhances the rotational invariance of BBO.A novel CMM-BBO approach is developed.Numeric simulations show CMM-BBO effectively improves the performance of BBO. Biogeography-based optimization (BBO) is a new evolutionary algorithm. The major problem of basic BBO is that its migration operator is rotationally variant, which leaves BBO performing poorly in non-separable problems. To overcome this drawback of BBO, in this paper, we propose the covariance matrix based migration (CMM) to relieve BBOs dependence upon the coordinate system so that BBOs rotational invariance is enhanced. By embedding the CMM into BBO, we put forward a new BBO approach, namely biogeography-based optimization with covariance matrix based migration, called CMM-BBO. Specifically, CMM-BBO algorithms are developed by the CMM operator being randomly combined with the original migration in various existing BBO variants. Numeric simulations on 37 benchmark functions show that our CMM-BBO approach effectively improves the performance of the existing BBO algorithms.

Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method

This study focuses on the performance monitoring of a non-Gaussian process with multiple operation conditions. By utilizing the Bayesian inference technique, the proposed method, locality preserving sparse modeling, can automatically identify the current operation condition. Then, the feature of the data structure is extracted by locality preserving projections (LPP) and modeled by the sparse modeling technique. This hybrid framework of sparse modeling and LPP provides a robust and accurate paradigm for process data clustering and monitoring. The validity and effectiveness of this approach are verified by applying it to both a synthetic numerical example and the Tennessee Eastman process benchmark process.

Multiagent Systems on Multilayer Networks: Synchronization Analysis and Network Design

This paper is concerned with the synchronization of multiagent systems connected via different types of interactions, known as multilayer networks. Additive coupling and Markovian switching coupling are proposed to capture the layered connections with two kinds of mathematical models constructed. First, based on simultaneously diagonalization of multiple Laplacian matrices, a general criterion is derived, ensuring that the synchronization problem with additive coupling can be decoupled. Then, an alternative condition is presented, which is related to the number of layers, regardless of the number of agents. With the derived criteria, a concept of joint synchronization region is introduced and further discussed as a network design problem. Synchronization with Markovian switching layers is also analyzed in parallel, exemplified by some special cases of two-layer networks. Finally, a group of cellular neural networks coupled by two-layer connections are chosen to illustrate the effectiveness of the theoretical results.

Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry

Abstract Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④ life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.

Multiobjective evolutionary algorithm based on the Pareto Archive and individual migration

A multiobjective evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multiobjective functions, where individuals generated by tournament selection from the union of single objective and Pareto archive population form the single objective population of next generation. At each evolving iteration, based on the concept of Pareto dominance, a finite-sized Pareto archive population is iteratively updated and trimmed by a new crowded-comparison operation. Especially, individuals in Pareto archive population also join evolutionary operations to increase the converging speed and improve quality of nondominated solutions. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the nondominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individuals migrating from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision.

Genetic mechanism-enhanced standard particle swarm optimization 2011

Standard particle swarm optimization 2011 (SPSO2011) is a major improvement of the original particle swarm optimization (PSO) with its adaptive random topology and rotational invariance. Its overall performance has also been improved considerably from the original PSO algorithm, but further improvement is still possible. This study attempts to enhance the exploration ability of SPSO2011 further. The enhancement method conditionally introduces a new genetic mechanism to improve the personal best condition of each particle. This conditional event is called an event-triggered mechanism. Moreover, the new genetic mechanism is utilized to crossover, mutate, and select an improved offspring to improve the condition of the cognitive component and indirectly enhance the exploration ability. The proposed algorithm is called genetic mechanism-enhanced SPSO2011 (SPSO2011_GM). SPSO2011_GM is empirically analyzed with 42 benchmark functions. Results confirm the efficiency of the proposed enhancement method and verify the convergence, exploration, reliability, and scalability of the method.