Statistical Mechanics Approximation of Biogeography-Based Optimization.

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of 1s in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those of the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamics of general separable functions, and we find that the results obtained for separable functions are the same as those for the one-max problem. The statistical mechanics theory of BBO is shown to be in good agreement with simulation.

Adaptive bacteria colony picking in unstructured environments using intensity histogram and unascertained LS-SVM classifier.

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

A Novel Scalable and Reliable Control for DC Microgrids With Varying Number of Agents.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Secure Adaptive Event-Triggered Control for Cyber-Physical Power Systems Under Denial-of-Service Attacks.

Secure control for cyber-physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.

Cross-dimensional formation control of second-order heterogeneous multi-agent systems.

This paper is concerned with the cross-dimensional formation control of a second-order multi-dimensional heterogeneous multi-agent system. Agents are first separated into several groups according to their position/velocity vector dimensions. Then the cross-dimensional formation control problem is formulated such that agents in the same group form a time-varying formation in their own dimension and agents in different groups cooperatively move in multiple dimensions. This can make follower agents in different dimensions cooperatively track a leader. Moreover, a cross-dimensional formation protocol is designed based on full or partial information of neighboring agents. For higher-dimensional agents, full information of lower-dimensional neighbors is adopted. For lower-dimensional ones, only partial information of higher-dimensional neighbors is used. Furthermore, a necessary and sufficient condition for the second-order heterogeneous multi-agent system to achieve cross-dimensional formation is provided. Accordingly, a criterion for designing cross-dimensional formation protocol is further derived. Finally, under an undirected graph, a lower-dimensional protocol design criterion is obtained if there is no data exchange between lower- and higher-dimensional followers. The effectiveness of the obtained results is demonstrated through cross-dimensional target enclosing performance analysis for multiple robots and quadrotors.

Differential Human Learning Optimization Algorithm.

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

Distributed Fusion Estimation for Stochastic Uncertain Systems With Network-Induced Complexity and Multiple Noise.

This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the H2/H∞ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.

Secure Control of Networked Control Systems Using Dynamic Watermarking.

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Impact of PHEV in active distribution network under gas station network attack.

This paper proposes a novel method to analyze the impacts of plug-in hybrid electric vehicle (PHEV) charging on branch power flows and voltages of an active distribution network under gas station network attack. Specifically, when the gas station network is attacked and cannot provide refueling service, PHEVs running out of gasoline will be only driven in the electric vehicle (EV) mode, which will significantly increase PHEV charging load and lead to branch power flow increment and voltage drop or even voltage collapse in distribution network. To overcome the problem, the switch of PHEV operating mode (i.e., the EV mode and the combustion engine (CE) mode) is first analyzed by considering whether the remaining gasoline can satisfy daily gasoline consumption, and based on that, a novel model of the PHEV charging load is constructed. Then, an integrated approach including Nataf/normalization transformation and elementary transformation (ET) is employed to deal with the general correlation of spatially close distributed generations in the active distribution network. Furthermore, point estimate method (PEM) based probabilistic load flow (PLF) is used to analyze the impacts of PHEV charging on branch power flows and voltages of the active distribution network under gas station network attack. Finally, the proposed method is tested on a real coastal active distribution network, and simulation results verify that PHEV charging could result in continuous branch power flow increase and voltage decrease over a prolonged attack time. Moreover, the higher PHEV operating status (OS) leads to slower branch power flow growth and voltage drop, and a higher PHEV penetration level will exacerbate branch power flow increment and voltage limit violation over with the extension of the attack.

Real-Time H∞ Control of Networked Inverted Pendulum Visual Servo Systems.

Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H∞ disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H∞ controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.

A Multipopulation-Based Multiobjective Evolutionary Algorithm.

Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.

Network-Based T-S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles.

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.