This work investigates the synchronization problem of a dynamical network composed of nonlinear nodes with directed couplings and suffering from heterogeneous delays. Specifically, a time-varying pinning control is adopted to achieve synchronization. The system’s dynamics are interpreted under a multiplex network framework, from which an indicator is designed to measure the synchronizability of the pinned network. Criteria are then derived to ensure exponential synchronization while the largest admissible coupling delay is also estimated. These criteria are based on the time average of the proposed indicator over some user-defined time intervals. Therefore, it possesses high versatility to support a wide range of scenarios with different intermittent control strategies. An interesting observation is that the proposed indicator is always upper bounded, manifesting that if the couplings between the nodes are too weak, pinning synchronization may fail no matter how large the pinning gain is. To further demonstrate the applicability of the theorem and the corollaries, numerical examples under different pinning configurations are presented.

This work investigates the tracking consensus problem of multiagent systems over directed networks, where the control gains follow certain volatile patterns. Some event-based consensus protocols are formulated so as to reduce the redundant execution of control. By using an extended differential inequality with a time-dependent coefficient, criteria for tracking consensus under time- and state-dependent triggering conditions are constructed, respectively. It is proved that the time average of the control gain, together with the agent dynamics, network topology, and triggering conditions, governs the consensus despite the fluctuation of control gain. The derived theorem can be utilized to ensure consensus with intermittent strategies aiming to lessen the burden in communications, including aperiodic on-off control with periodic perturbation and pulse-modulated on-off control. Unlike existing works, the requirement of a positive lower bound of control ratios is removed and, thus, a wide range of control gain patterns is possible, signifying higher flexibility in intermittent policy design. Finally, numerical examples are provided to further illustrate the theoretical results.

A two-stage stochastic programming model is established to minimize EV's expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimally distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.

This brief investigates the master-slave synchronization problem of delayed neural networks with general time-varying control. Assuming a linear feedback controller with time-varying control gain, the synchronization problem is recast into the stability problem of a delayed system with a time-varying coefficient. The main theorem is established in terms of the time average of the control gain by using the Lyapunov-Razumikhin theorem. Moreover, the proposed framework encompasses some general intermittent control schemes, such as the switched control gain with external disturbance and intermittent control with pulse-modulated gain function, while some useful corollaries are consequently deduced. Interestingly, our theorem also provides a solution for regaining stability under control failure. The validity of the theorem and corollaries is further demonstrated with numerical examples.

This paper is devoted to the consensus problem of networked nonlinear agents with multiple self-delays and time-varying coupling. We first establish a generalized Halanay's inequality based on comparison theorem, and then convert the consensus problem into a stability problem of retarded differential equation with time-varying coefficients, from which sufficient conditions for consensus are derived. Our results manifest that the time-average of coupling strength over intervals of certain length, together with the underlying topology and the values of self-delays, plays a crucial role in guaranteeing consensus. Based on the theoretical analysis, an estimation of the largest admissible delay is also available. This paper provides a general framework for achieving consensus in agents with time-varying coupling, such as coupling with external perturbations, intermittent control in on-off fashion, or pulse-modulated coupling strength, etc. Furthermore, useful criteria are given for various applications which have also been verified with numerical simulations.

In this work, the consensus problem of networked agents under denial-of-service (DoS) attacks is studied. Due to DoS attacks, communication channels would be intermittently congested, which inevitably affects the state update in the consensus process and leads to the failure of a consensus protocol. Such a situation has been recast into a consensus problem with on-off control, as suggested by other studies. However, it is commonly assumed that the underlying graph is undirected and the DoS attack follows certain periodic patterns. Obviously, these assumptions cannot truly reflect some real cases. Therefore, aperiodic attacks and directed topology are considered here. Our theoretical results reveal that consensus is achievable, provided that the average of the DoS attack duration over a certain length of time interval is upper bounded. Compared with other existing results, this new consensus criterion demonstrates better generality and tractability. Finally, simulations are presented to further demonstrate the effectiveness of the theoretical results.

Network optimization problems are usually NP-hard and evolutionary algorithms (EAs) are good at these problems. However, direct implementation of EA, ignoring the network properties or problem features, is common which usually results in a low-efficient algorithm. In this paper, a framework of feature-embedded evolutionary algorithm is outlined and the idea is demonstrated by embedding the nodal centralities in a genetic algorithm (GA) for solving the node selection problem in pinning control. Simulation results confirm that the new design outperforms existing deterministic schemes in terms of solution quality, and significant improvements are also noticed as compared to classical GA. The results also shed light on high efficient feature-embedded EAs in solving other challenging optimization problems.

Different from the classic differential evolution (DE) and many of its variants, collective information powered DE (CIPDE) is characterized by the utilization of collective information of population in the mutation and crossover processes of DE. This paper proposes a restart mechanism for CIPDE, to improve its robustness for solving the 100-digit challenge on single objective numerical optimization. Restart based CIPDE (rCIPDE) restarts the population when the unsuccessful update of the population exceeds a threshold value. Simulations on the challenge show that the restart mechanism enhances the performance on five out of the total 10 benchmark functions. According to the competition rule, rCIPDE achieves a total score of 85 (10 marks on each of F1-F7 and F10, 2 marks on F8 and 3 marks on F9).

As one of the promising initiatives for environmental preservation, electric vehicles (EVs) have been deploying in many countries. However, the limited drivable range of EV usually necessitates mid-way re-charging for long-distance journey, which is one of the most concerning issues in EV driving. Given limited public charging resources, EV drivers easily suffer from serious “range anxiety”, in particular when the queuing time at the charging station is long. Therefore, it is important to distribute the EV flows to the right charging stations for recharging, while at the same time, to minimize the overall journey time. In this paper, such an issue is formulated as an optimization problem and solved by a dedicated flow distribution algorithm (FDA). The practical usage of the design is demonstrated by extensive simulations upon the eastern Massachusetts highway network.

For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commercial usage. In this paper, a dynamic electric vehicle routing problem (D-EVRP) model is designed for planning the itinerary for goods delivery by the utilization of EVs in logistics industry. To reflect the real situation, the D-EVRP considers a time-dependent stochastic traffic condition and captures the discharging/charging pattern of an EV using an analytical battery model. Its aim is to minimize the overall service duration, subject to a variety of the state-of-art constraints common in EV routing problems. Furthermore, to address the D-EVRP, a hybrid rollout algorithm (HRA), which incorporates a dedicated pre-planning strategy and a rollout algorithm, is also proposed. The effectiveness of the HRA and benefits of incorporating the analytical battery model are justified by extensive simulations using the real-world D-EVRP instances.

The rapid development in logistics industry urges for an effective express delivery network, to deliver goods to customers in a prompt and cost effective way. Such a network is usually operated in a typical hub-and-spoke manner. Therefore, it is important to assign the hubs (known as transshipment points (TP)) in the network so as to maximize its performance. In this paper, the TP assignment problem is formulated as a multiobjective problem and solved by a multiobjective genetic algorithm (MOGA). The practical usage of the design is demonstrated with a China express delivery network and its effectiveness is confirmed by comparing with a weighted exhaustive heuristic algorithm. As shown by the simulation results, our approach is very effective and a widely spread non-dominated solution set can be obtained.

Many artificial networks in the real world have the power-law degree distribution. This feature causes a lot of difficulties in network design, such as robustness, reliability and other performance issues. In this paper, the congestion problem in power-law communication networks is focused. Since nodes with very large degree usually become the bottleneck in this kind of networks when shortest path routing is adopted, a new design of routing is proposed. A hybrid approach, that combines a genetic algorithm and a greedy approach, is designed to find the routing paths of the source-destination pairs. As shown by theoretical and simulation results, the transmission capacity can be improved with the new design which outperforms other existing routing schemes. It should also be emphasized that, though only scale-free network is discussed, the proposed approach is general and useful for other network types.

In this paper, a genetic algorithm (GA) is proposed to tackle with the constrained pinning control problem in complex networks. Based on a hierarchical chromosome structure, it is demonstrated that suitable pinned nodes can be selected with appropriate control gains, resulting in a better synchrony capability of the entire network. Simulation results verify the effectiveness of this method, and it is concluded that the proposed approach can outperform some other evolutionary algorithms and the conventional node selection approaches.

In this paper, a novel genetic algorithm (GA) is presented to tackle with the constrained pinning control problem for complex networks. The information of the node degree is incorporated in the design so that a set of pinned nodes can be duly obtained. In addition, the control strength of each selected node is assigned by the use of a decrease-and-conquer approach, satisfying the total control strength constraint of the problem. Simulation results confirm the effectiveness of the design and it outperforms other evolutionary algorithms and conventional node selection approaches.

Recently, gene transpositions have gained their power and attentions in computational evolutionary algorithm designs. In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first proposed and two new gene transposition operations, namely, cut-and-paste and copy-and-paste, were introduced. Although the outperformance of JGGA has been demonstrated by some detailed statistical analyses based on numerical simulations, more rigorous theoretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical justifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimization algorithms.

In this paper, a novel model for opinion dissemination over network is suggested so that market penetration activities can be described. The model assumes the existence of two different products, one of which is always considered first and the supporters' (or users') opinions are allowed to propagate among individuals of the population. This is to describe the dynamics of market penetration of a particular product, by gaining the market share of its competitor or attracting new users, even the competitor is with higher reputation. From the simulation results, it is found that the product with lower preference can still obtain a non-zero fraction of market share in many cases, when a small-world network is considered. Some level of clustering in the network facilitates the adoption of the product, while increasing randomness undermines its existence. The simulations also show the importance of the users' remembrance of a product and the communication effectiveness in related to market penetration.

A new approach for chaos control using periodic impulsive parametric perturbations is suggested and applied to suppress chaos in Uuffing system. Using the Melnikov's method, it is proved that in the neighborhood of a homoclinic orbit, chaos can he suppressed perturbing impulsively a parameter of the system. A sufficient condition for chaos suppression is derived, and the amplitude and width of the impulsive perturbation applied is numerically obtained.

In this paper, two methodologies are proposed to formulate high-dimensional chaotic maps based on the conventional two-dimensional Cat map. It is proved that these new generated chaotic maps not only have similar properties to their parental map, such as area-preservation and chaotic, they also possess a better mixing nature and high complexity, granting them to be good potential candidates for practical applications.

Based on the sawtooth wave function, this paper initiates an approach for generating novel multi-wing butterfly chaotic attractors from the generalized first and second types of modified Lorenz systems. Our theoretical analysis shows that every index-2 saddle-focus equilibrium corresponds to a unique wing in the butterfly attractors. Compared with the traditional ring-shaped multiscroll Lorenz chaotic attractors, the proposed multi-wing butterfly chaotic attractors are much easier to be constructed and implemented by analog circuits. Furthermore, a module-based unified circuit diagram is designed for realizing various multi-wing attractors.

We observe that an effect of ldquodisk resource sharingrdquo of multi-copy movie traffic has great impact on the blocking performance of a video-on-demand system. This observation leads us to establish a conjecture on how to balance the movie traffic load among ldquocombinationrdquo groups of disks to maximize the level of disk resource sharing. For a given file replication instance, the conjecture predicts in general an effective lower bound on the blocking performance of the system. It motivates the design of a numerical index that measures quantitatively the goodness of disk resource sharing on allocation of multi-copy movie files. It also motivates the design of a greedy file allocation method that decides a good quality heuristic solution for each feasible file replication instance. We further develop analytical formulas to obtain approximate results for the bound fast and accurately. These techniques can be utilized by an optimization program to find near-optimal file assignment solutions for the system computationally efficiently.

A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopted for evolutionary computing is outlined. The procedures for executing the computational optimization are provided. A large number of constrained and unconstrained test functions have been utilized to verify this new scheme. Its performances on convergence and diversity have been statistically examined and comparisons with other evolutionary algorithms are carried out. It has been discovered that this new scheme is robust and able to provide outcomes quickly and accurately. A stringent measure of binary-indicator is also applied for algorithm classification. The outcome from this test indicates that the JG paradigm is a very competitive scheme for multiobjective optimization and also a compatible evolutionary computing scheme when speed in convergence, diversity, and accuracy are simultaneously required.

We present a genetic algorithm for tackling a file assignment problem for a large-scale video-on-demand system. The file assignment problem is to find the optimal replication and allocation of movie files to disks so that the request blocking probability is minimized subject to capacity constraints. We adopt a divide-and-conquer strategy, where the entire solution space of file assignments is divided into subspaces. Each subspace is an exclusive set of solutions sharing a common file replication instance. This allows us to utilize a greedy file allocation method for finding a good-quality heuristic solution within each subspace. We further design two performance indices to measure the quality of the heuristic solution on 1.) its assignment of multicopy movies and 2.) its assignment of single-copy movies. We demonstrate that these techniques, together with ad hoc population handling methods, enable genetic algorithms to operate in a significantly reduced search space and achieve good-quality file assignments in a computationally efficient way.

The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.

Active research in bio-inspired neurons has been observed in the last decade. In this paper, we are interested in identifying the topology of a biological neural network based on an adaptive observer design. It is proved that the topological structure of a network and the connectivities of its neurons can be acquired by observing the neurons' dynamics. That can also be used to monitor and report any changes of the topological structure. The effectiveness of this approach is successfully demonstrated with a network of coupled Hindmarsh-Rose neurons.

In this paper, a general structure based on Chua's circuit framework is proposed for the generation of n×m scroll attractors. Starting from a modified Chua's circuit with n-scroll attractor, n × m-scrolls can be duly generated and observed by adding a simple staircase function onto the state equations. This method is successfully applied to different n-scroll Chua's circuits with equi-distance equilibrium points, and the results are verified by both simulations and experiments.

In this paper, a jumping-genes paradigm is proposed for optimizing the wireless local area network for an integrated-circuit factory. Through the base station placement, not only the best quality of service of the network is guaranteed, but also the performance of the network can be a tradeoff with the number of allowable base stations. This provides a greater flexibility for the designer when the factory environment such as physical space, building structure, equipment, and cost are the significant parts of the overall design criteria. The main feature of this optimization scheme is its capacity to yield the extreme minmax solutions under a specific allowable design, power-loss threshold. It provides a much wider range of solutions for selection, which includes the ultimate low-cost design without sacrificing the performance or vice versa. The obtained results revealed from this study indicated that the jumping-genes paradigm is an effective and reliable methodology for this type of design problem

In this paper, a newly developed jumping genes evolutionary paradigm is proposed for optimizing the multiobjective resource management problem in direct sequence–wideband code division multiple access systems. This formulation enables both total transmission power and total transmission rate to be simultaneously optimized. Since these two objectives are conflicting in nature, a set of tradeoff non-dominated solutions could be obtained without violating the quality of service. This new algorithm has been statistically tested and compared with a number of various multiobjective evolutionary algorithms including the use of binary ε-indicator for classifying the capability in generating the quality of non-dominated solution sets. In addition, the capacity of finding a number of extreme solutions is an extra indication to show its ability to measure the diversity along the Pareto-optimal solution front in a unique fashion.

Little and Venkatesh conjectured that, for an interactive VoD system with a single random trial resource selection scheme, the blocking probability of a user's request is minimized when the overall movie traffic load is spread uniformly on each disk in the system. In this paper, we generalize this conjecture to the situation where there can be repeated random trials or where a least busy fit resource selection scheme is used. We support our conjecture with a simulation of a realistic system, and propose a metric following the idea of our conjecture to assess the goodness of movie assignment in the system.

We consider a video-on-demand system in which multiple copies of each movie file are kept in separate disks. Various schemes for file allocation and retrieval are considered and compared. We have introduced a certain easy-to-implement scheme that requires 15% more storage than another certain harder-to-implement scheme.

Resource-demanding services such as multimedia-on-demand (MOD) become possible as broadband Internet is getting more popular. However, as the size of multimedia files grows rapidly, storage of such large files becomes a problem. Since multimedia contents will generally become less popular with time, it is desirable to design a prediction algorithm so that the multimedia content can be unloaded from the server when it becomes unpopular. In this paper, we have two objectives: 1) analyse the MOD viewing trend in order to understand the viewing behaviour of users; 2) predict the viewing trend based on the knowledge obtained from the trend analysis. For trend analysis, we study three traditional regression models, including linear regression, exponential regression, and power regression, and propose two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), to improve the goodness of fit. Then, the most fitted models are used in trend prediction. Four prediction approaches, fixed regression selecting (FRS), continuous regression updating (CRU), historical updating (HU), and continuous regression with historical updating (CRHU) are proposed. From the numerical results, we find that CRHU, which is constructed by considering historical trends and new incoming viewing request data, is in general the best method in forecasting MOD trend.

As broadband networks keep increasing in transmission speed, multimedia-on-demand (MOD) is expected to be one of the most popular broadband services in the near future. MOD systems can provide two types of services: interactive service and batch service. The interactive service provides each user with one dedicated stream. With it, the user can use the VCR function controls such as pause, forward and rewind. However, the service is expensive and not scalable to the system size. With the batch service, the users who request the same multimedia content within a batch period will be grouped together and served by one dedicated stream. Hence, the system can either use fewer resources or serve more customers. However, batch users must wait for programs to begin and are unable to perform VCR function control. We take advantage of both services by considering an MOD system that provides both services. Also, we propose two methods to optimize their coexistence. In addition, an analytical model is derived for performance evaluation. Numerical results show that the proposed hybrid system really outperforms both individual systems. The hybrid system provides a superior solution to both the service providers and the users. Specifically, given a limited storage resource, the service provider receives more revenue by serving more users concurrently, while the users have a satisfactory viewing experience due to higher availability of programs and the low average waiting time.

Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a Parallel Genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential Genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner23 is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.