Jianhui Lv

Solving 0-1 knapsack problem by greedy degree and expectation efficiency

Graphical abstractDisplay Omitted HighlightsThe idea based on region partition of items for solving 0-1 knapsack problem.Greedy degree algorithm for putting some items into knapsack early.Dynamic expectation efficiency model for obtaining the candidate objective function value.Static expectation efficiency model for updating the objective function value.The proposed algorithm in this paper has correctness, feasibility, effectiveness, and stability. It is well known that 0-1 knapsack problem (KP01) plays an important role in both computing theory and real life application. Due to its NP-hardness, lots of impressive research work has been performed on many variants of the problem. Inspired by region partition of items, an effective hybrid algorithm based on greedy degree and expectation efficiency (GDEE) is presented in this paper. In the proposed algorithm, initially determinate items region, candidate items region and unknown items region are generated to direct the selection of items. A greedy degree model inspired by greedy strategy is devised to select some items as initially determinate region. Dynamic expectation efficiency strategy is designed and used to select some other items as candidate region, and the remaining items are regarded as unknown region. To obtain the final items to which the best profit corresponds, static expectation efficiency strategy is proposed whilst the parallel computing method is adopted to update the objective function value. Extensive numerical investigations based on a large number of instances are conducted. The proposed GDEE algorithm is evaluated against chemical reaction optimization algorithm and modified discrete shuffled frog leaping algorithm. The comparative results show that GDEE is much more effective in solving KP01 than other algorithms and that it is a promising tool for solving combinatorial optimization problems such as resource allocation and production scheduling.

Ant Colony Optimization-Inspired ICN Routing with Content Concentration and Similarity Relation

In this letter, we propose a novel Ant Colony Optimization (ACO)-inspired Information-Centric Networking (ICN) Routing mechanism based on Content concentration and Similarity relation (AIRCS). At first, we propose a continuous content concentration model to conduct the interest forwarding. Second, we propose a similarity relation model between two content routers to act as a heuristic factor to facilitate the interest forwarding. Third, we propose a computation scheme about the forwarding probability with content concentration and similarity relation to determine which outgoing interface is used to forward interest request. Finally, we design the ICN routing mechanism based on the probabilistic forwarding to retrieve the closest content copy. The experimental results show that AIRCS has good performance.

ACO-Inspired ICN Routing Mechanism with Mobility Support

Abstract Nowadays, Information-Centric Networking (ICN) has been accepted as a promising paradigm in which users retrieve named content with information-centric communication mode rather than finding IP address with host-centric communication mode. Although ICN routing has attracted much attention from researchers, the current proposals cannot effectively and intelligently solve the mobility problem in a self-adaptive and self-organizing manner. In this paper, we introduce Ant Colony Optimization (ACO) into ICN and propose a novel ACO-inspired ICN Routing mechanism with Mobility support (AIRM) to retrieve the content no matter where it moves. At first, a continuous pheromone updating strategy inspired by alcohol volatilization model is devised to conduct the forwarding of interest ant. Secondly, we determine which outgoing interfaces can be used to forward interest ant, propose a computation scheme to obtain the forwarding probability, and select an outgoing interface to forward interest ant by roulette model. Thirdly, the detailed design on AIRM is presented to address mobility while retrieving the closest content copy. Finally, we evaluate the proposed AIRM, and the simulation results show that AIRM not only solves the mobility problem effectively but also has better performance than existent schemes in terms of routing success rate, routing hop count, load balance degree and execution time.

Energy-efficient ICN routing mechanism with QoS support

Abstract Information-Centric Networking (ICN) brings a promising networking paradigm which changes host-centric communication mode, and its routing decision depends on the unknown and named content item rather than the known IP address. In this paper, we propose a novel Energy-efficient Quality of Service (QoS) Routing mechanism for ICN (EQRI). Firstly, we evaluate the suitability of link state to user’s QoS requirements by Cauchy distribution model and formulate the energy efficiency of link by monitoring the corresponding traffic. Secondly, we design a priority determination strategy based on QoS and energy efficiency, a color management strategy to assign color for outgoing interface, and a backtracking strategy to cope with the failed Interest packet. Thirdly, we propose a link selection algorithm based on color management, priority determination and backtracking strategy. Finally, we devise an ICN routing mechanism which consists of Interest packet routing and Data packet routing. The experimental results show that EQRI not only retrieves the content effectively but also outperforms existent mechanisms.

RISC: ICN routing mechanism incorporating SDN and community division

Abstract Information-Centric Networking (ICN) is one promising architecture paradigm which is a profound shift from address-centric communication model to information-centric one. Although ICN routing has attracted much attention from researchers, there are few researches on improving it inspired by other fields. In this paper, we propose a Routing mechanism for ICN incorporating Software-Defined Networking (SDN) and Community division (RISC), by decoupling control plane from data plane and dividing ICN topology into different communities. Firstly, we propose a community division scheme based on maximal tree in order to help retrieve the content conveniently and effectively. Secondly, we place all information about contents and forwarding into the corresponding information center for the centralized management. Thirdly, we design a routing mechanism which consists of intra-community routing based on same community information and inter-community routing based on social relationship among communities. Finally, the experimental results show that the proposed RISC not only speeds up content retrieval but also outperforms existent methods.

ACO-inspired Information-Centric Networking routing mechanism

Abstract In recent years, the bio-inspired solution has been employed to address routing optimization issue intelligently without manual intervention. In this paper, we propose a novel Ant Colony Optimization (ACO)-inspired Information-Centric Networking (ICN) Routing mechanism (ACOIR) by mapping ACO into ICN. At first, we devise a content management strategy based on the storage of name prefix to help conveniently and effectively manage and provide contents. Secondly, we propose a continuous model for content concentration by considering dynamic environment to conduct interest forwarding. Thirdly, we give a computation scheme about forwarding probability with physical distance and content concentration considered to determine the forwardable outgoing interface. Finally, we propose a comprehensive routing mechanism based on probabilistic forwarding to retrieve the most suitable content copy. We evaluate the proposed ACOIR, and the experimental results demonstrate that ACOIR can obtain the optimal solution and has better performance than other methods.

Accomplishing Information Consistency under OSPF in General Networks

In this paper, we design an LAP based routing algorithm in General Networks (GN) to solve the problem of information consistency of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk, (ii) re-composting the SNs to a network with regular topology structure by adding links, (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding, and (iv) processing different converged network topologies with different LAP based routing algorithms. The proposed algorithm is compared with Dijkstra algorithm over some random network topologies. Simulation results show that the proposed algorithm can solve the problem of information consistency of the full network under OSPF and has better performance than Dijkstra algorithm.

PCA-based PSO-BP neural network optimization algorithm

BP neural network inherits many disadvantages such as slow convergence speed and easily converging to local minimum. The input data generally has a high-dimensional feature. To improve the performance of neural network, we propose a novel algorithm. Before inputting the data into the neural network, this algorithm reduces the dimension of the data with PCA algorithm. Then, this algorithm simplifies the structure of the neural network and reduces the amount of computation combined with PSO-BP algorithm. Simulation results experiments demonstrate that the proposed algorithm improves the overall efficiency of neural networks, which proves that PCA-Based PSP-BP algorithm is better than PSO-BP algorithm.

LAPGN: Accomplishing information consistency under OSPF in General Networks (an extension)

Abstract Open Shortest Path First (OSPF) protocol is a link-state routing protocol which requires the link-state information to be synchronous, and it needs to achieve a fast convergence when the network topology changes. In this paper, we extend the Limitation Arrangement Principle (LAP) algorithm to General Networks (GN) and design an LAP-based routing algorithm in GN (LAPGN) to solve the information consistency problem of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk; (ii) re-composting the SNs to a network with regular topology structure by connecting networks and adding links; (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding; and (iv) processing different converged network topologies with different LAP-based routing algorithms. We compare the proposed algorithm with the well-known OSPF algorithm over some random network topologies and six backbone topologies. The simulation results reveal that LAPGN can solve the information consistency problem and has better performance than OSPF algorithm.

Heuristics-based influence maximization for opinion formation in social networks

Abstract Influence maximization for opinion formation (IMOF) is a significant issue in social networks. In general, the main goal of the IMOF is to determine a set of optimal nodes in an effective way for the maximum propagation of the most ideal opinions. The current studies mainly focus on the control methods for the opinion formation, for example, the informed agents, the improved opinion formation models and the effective initial-node-determination algorithms. However, there are few researches on the mathematical modeling of the IMOF. Additionally, the effective mechanisms should be specifically designed to solve the IMOF. In this paper, a general IMOF model is formulated by the informed agents, and then a 3-hop heuristic algorithm is proposed to deal with the IMOF. Firstly, the IMOF is formulated mathematically as an optimization model. Then, in order to satisfy various preferences among nodes, the weighted bounded confidence model is devised to calculate the opinion of each node effectively. Moreover, the influence spread obeys 3-hop rule, and node influence gradually dissipates and stops beyond 3 hop. Therefore, based on node influence, a 3-hop heuristic algorithm is proposed to effectively determine the top-m influential nodes. With the opinion set initialized, a discount strategy is presented to accelerate the propagation of the selected top-m influential nodes. Finally, the experimental results demonstrate that the proposed method obtains better performance on the average of opinions than the chosen benchmarks.