Space Information Network can not only provide wireless services for global users, but also be regarded as means of information communication in space scientific exploration. To adapt the increasing demand of multimed...
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ISBN:
(纸本)9781538637906
Space Information Network can not only provide wireless services for global users, but also be regarded as means of information communication in space scientific exploration. To adapt the increasing demand of multimedia services, this paper introduces Software Defined Networking (SDN) into Space Information Network to simplify the payload of satellite, improve routing computation efficiency and reduce the network routing cost, etc. We build the network model based on the constellation design. Moreover, we adopt the virtual topology method to generate the topology snapshots to overcome the dynamic change of topology in Space Information Network. And snapshots merging algorithm is designed to reduce the number of snapshots, which reduces the number of calculations for routing tables and network consumption. At last, we improve two algorithms: Amoeba algorithm and ripple-spreading algorithm and then we design amoeboid-ripple routing strategy consisting of these two algorithms. Simulation results showed that the proposed routing mechanism has lower packet loss rate and traffic failure rate, especially has fewer routing hops than the benchmark mechanism when network load is heavy.
Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (...
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Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.
How to allocate and use resources play a crucial role in disaster reduction and risk governance(DRRG).The challenge comes largely from two aspects: the resources available for allocation are usually limited in quantit...
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How to allocate and use resources play a crucial role in disaster reduction and risk governance(DRRG).The challenge comes largely from two aspects: the resources available for allocation are usually limited in quantity; and the multiple stakeholders involved in DRRG often have conflicting interests in the allocation of these limited resources. Therefore resource allocation in DRRG can be formulated as a constrained multiobjective optimization problem(MOOP). The Pareto front is a key concept in resolving a MOOP, and it is associated with the complete set of optimal solutions. However, most existing methods for solving a MOOPs only calculate a part or an approximation of the Pareto front, and thus can hardly provide the most effective or accurate support to decisionmakers in DRRG. This article introduces a new method whose goal is to find the complete Pareto front that resolves the resource allocation optimization problem in *** theoretical conditions needed to guarantee finding a complete Pareto front are given and a practicable, ripplespreadingalgorithm is developed to calculate the complete Pareto front. A resource allocation problem of risk governance in agriculture is then used as a case study to test the applicability and reliability of the proposed method. The results demonstrate the advantages of the proposed method in terms of both solution quality and computational efficiency when compared with traditional methods.
Travelling in adverse weathers is a dynamical path optimization problem (DPO), as weather conditions usually keep changing during the traveling period. A common practice of DPO is to conduct real-time online path opti...
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ISBN:
(纸本)9781509040933
Travelling in adverse weathers is a dynamical path optimization problem (DPO), as weather conditions usually keep changing during the traveling period. A common practice of DPO is to conduct real-time online path optimization based on the current weather conditions. However, this can hardly lead to optimal actual travelling trajectory under a given weather dynamics, because the online optimization based on the current weather conditions is rarely optimal for future weather conditions. This paper is concerned with, given a weather dynamics, how to achieve optimal actual travelling trajectory by just a single offline optimization. To this end, the concept of co-evolutionary path optimization (CEPO) is introduced, where weather conditions are not static, but keep changing during a single run of offline optimization. Existing DPO methods can hardly address CEPO, because in a single run of online optimization, existing DPO methods treat weather conditions as static, i.e., existing DPO methods do not consider/allow weather conditions to change in a single run of online optimization. To address the CEPO in dynamical adverse weathers, this paper proposed a ripple-spreading algorithm (RSA), which can achieve optimal actual travelling trajectory by a single offline calculation. The reported CEPO and RSA are then tested on a typhoon scenario in Hainan Province of China, and the advantages against traditional DPO are clearly demonstrated.
It is a challenging task to find the k shortest paths in a time-window network, where a node may have some specific time windows only within which is the node accessible. Existing research assumes that a traveller can...
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ISBN:
(纸本)9781509006229
It is a challenging task to find the k shortest paths in a time-window network, where a node may have some specific time windows only within which is the node accessible. Existing research assumes that a traveller can pass through an accessible node immediately or wait to pass at start times of future time windows at the node. This paper targets at a more general case where a traveller, once arriving at a node, may choose to pass through the node at any discrete time in the time windows of the node. In such a generalized time-window network, the degree of complexity increases significantly, as the size of solution space soars up exponentially. By mimicking the natural ripple-spreading phenomenon on a liquid surface, we propose an effective ripple-spreading algorithm (RSA) for the k shortest paths problem in a generalized time-window network (k-SPPGTW). Besides one-to-one k-SPPGTW, the RSA is also extended to one-to-all k-SPPGTW, where all the k shortest paths from a given source to every other node in the network need to be found. The new method has a theoretical guarantee of optimality. The computational complexity of the reported RSA is just O(kxN(ATU)xN(L)), where N-L is the number of links in the network, and N-ATU is the average simulated time units for a ripple to travel through a link. The effectiveness and efficiency of the reported RSA for the k-SPPGTW are demonstrated by some preliminary experimental results.
Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective funct...
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Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.
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