Optimal Path Selection Problem Research Articles

Improved Triboelectric Nanogenerators for Self-Powered Systems in Flexible Electronic Devices

As the number of flexible electronic devices grows dramatically, the energy harvesting and storage technology of flexible electronic devices also urgently needs to advance. Traditional flexible electronic devices are battery-powered, which not only suffer from short endurance and large size, but also do not meet the new era requirement of environmental protection and energy saving. Therefore, this study investigates the self-powered system in flexible electronic devices using the Triboelectric Nanogenerator (TENG) technology. The research innovatively optimizes the TENG, proposes a liquid metal triboelectric Nanogenerator (LMTENG), and optimizes the optimal path selection problem. In the test results, the optimal output power of 567.3 μW is obtained when the contact frequency is 2 Hz for an external load of 15 MΩ. By comparing with the current TENG, the LMTENG significantly improves the optimal output power. On the other hand, the LMTENG is able to respond to a pressure of 22 kPa and maintain normal performance at 50% stretching. The study provides new ideas for improvement of triboelectric nanogenerators and also contributes to the optimization of flexible electronic devices.

Geo-Economic Analysis Based on an Improved Ant Colony Optimization

This paper addresses the optimal path selection problem for economic corridors, which is a significant issue in the field of geo-economics. The paper has utilized the spatiotemporal characteristics of geo-economics and identified the development needs in this field to propose an improved ant colony optimization (ACO) strategy. The proposed strategy focuses on enhancing the heuristic function, functional area setting, and pheromone updating strategy. The heuristic factors and transfer probabilities have been improved to couple the path nature, which were based on an analysis of the factors that influence geo-economics. This improvement enhances the applicability of the ACO to path selection problems in macrospace. Additionally, the paper has differentiated the priority of path nodes by setting functional areas, which adds directionality to path selection. The improved ACO has been applied to analyze the optimal path in macroscopic economic space. The experimental validation was conducted in the Indo-Pacific region and economic corridors in China within this region, and corresponding potential geo-economic hubs were analyzed. The experimental results were validated using the Mann−Whitney U test and an evaluation method based on path effectiveness. The feasibility and objectivity of the proposed method were verified. This research provides a valuable exploration of the problem of path selection in macrospace and time and provides decision aid for the construction and adjustment development of a country’s geo-economic relations in a given region. It is a technical reference for establishing sustainable development strategies and national and regional economic planning. Overall, this work contributes significantly to the field of geo-economics and demonstrates the effectiveness of the proposed method through experimental validation.

A Service Function Chain Deployment Method Based on Network Flow Theory for Load Balance in Operator Networks

Network function virtualization technology makes the deployment and management of network service more flexible and elastic by decoupling network function from dedicated hardware. The service requests of network function virtualization are usually deployed in the form of a service function chain. In order to solve the problems of load imbalance, unreasonable utilization of substrate resources, and the high delay of the service function chain deployment in operator networks, a service function chain deployment method based on the network flow theory is proposed in this paper. First, on the basis of perceiving the substrate network resources and topology with a software-defined network controller in real time, a candidate node set is determined according to the resource constraints and the locations of ingress/egress switch nodes that service flow flows in/out. Second, the candidate node set, the ingress/egress switch nodes and the connection between them are used to form a directed network, and the service function chain deployment problem is transformed into an optimal path selection problem. Then, a node disassembling method is used to transform the directed network into a capacity-flow-cost network. Finally, a minimum-cost maximum-flow algorithm is used to find the optimal deployment path and complete the service function chain deployment. Experiments show that the method proposed in this paper can guarantee the load balance of operator networks, reduce the average transmission delay of service flow, and make the utilization of substrate resources more reasonable.

Spectrum-aware outage minimizing cooperative routing in cognitive radio sensor networks

This paper investigates the optimal path selection problem for end-to-end (e2e) outage probability minimization in clustered cognitive radio sensor networks. In order to improve outage performance of the optimal path, under a high node density regime, we consider feasibility of virtual multiple-input single-output (v-MISO) links in addition to conventional single-input single-output (SISO) links in the path. Since sensor nodes in such networks are allowed to access the spectrum of the primary network only in an opportunistic manner, the path selection problem is studied under the constraints of probabilistic interference to PU receivers and only single use of any PU channel along the path. The above problem is formulated as a joint hop-constrained routing, spectrum assignment and transmit power control problem. A convex optimization framework is used to find a closed form expression for the optimal transmit power of each transmitting node along the optimal route. Extension of the analytical result facilitates design of a novel routing algorithm, called spectrum aware-minimum outage intelligent cooperative routing (SA-MOICR) algorithm, which not only selects the minimum outage path for a given routing session, but also determines the number of nodes and the unique PU channel to be used for transmission in each hop along the path. Simulation results are found to corroborate our analytical results and quantify the significant improvement of the SA-MOICR scheme over only SISO or only v-MISO based routing solutions in terms of the achievable e2e outage probability.

A hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection

AbstractThe shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency.

A Multi-Weights Ant Colony Algorithm for Solving Optimal Path in Tourism

Optimal path selection is a fundamental problem in tourism, the influence factors of which only including the rout length, but also including weather, transportation and the scenery of attractions and other relevant factors. Therefore, route selection only based on the route length cannot capture the actual requirement. The paper studies the multi-weights (such as weather, route length, attractions scenery and etc.) in route selection, and then proposed an improved ant colony algorithm based on multi-weights (ACA-MW), which uses the multi-weights ant and the genetic variation to search optimal path. Simulated experiment of the ACA-MW shows high performance, the improved algorithm is effective. In tourism, ACA-MW can do well in optimal path selection problem.

An adaptive algorithm for QoS-aware service composition in grid environments

Service composition enables users to realize their complex needs as a single request and it has been recognized as a flexible way for resource sharing and application integration since the appearance of Service-Oriented Architecture (SOA). For each of the needed individual services there may be many candidate services available presented by different vendors and with different functional and non-functional properties such as Quality of Service (QoS). Approaches are needed to select candidate services with various QoS levels according to user’s performance requirements meanwhile adapt to dynamic churn in grid environments. This paper mainly focuses on adaptive management of QoS-aware service composition in grid environments and proposes an adaptive algorithm for QoS-aware service composition (AQSC). In AQSC we model this problem as the Multi-Constrained Optimal Path selection problem (MCOP) and use heuristic approach for service selection, then backup services set is introduced as an adaptive mechanism so as to ensure the fulfillment of composite service when some candidate services fail or withdraw. Both theoretical analysis and simulation results indicate that AQSC has high composition success rate, finish rate and low cost.