Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Evolutionary algorithms for solving multi-objective shortest path problem -Case study of vehicle navigation problems

Finding Multi-objective shortest paths (MOSP) is an important problem in computer and transportation networks. MOSP is an NP-hard problem when it contains more than two objectives. MOSP problem can be e ciently solved using the evolutionary algorithms (EAs). The existing EAs are of two types: Population-based and single-solution-based. Population-based EAs are memory- intensive and single-solution-based EAs cannot yield good quality solutions within a small amount of time. We proposed two new EAs to solve the MOSP problem and overcome the shortcomings of the existing EAs. The proposed EAs require lesser memory and at the same time can also yield good quality solutions. The rst algorithm is based on Stochastic Evolution (StocE) and works on a single solution. It considers di erent sub-paths in the solution as its character- istics and eliminates bad sub-paths from generation to generation. The second proposed algorithm is an o -spring non-storing GA which is memory-e cient than the existing GAs and its variants. Unlike existing GA-based algorithms it does not store children chromosomes in the memory. In the proposed GA- based algorithm, the children chromosomes conditionally replace their parent chromosomes and thus do not need to be stored at new memory locations. The quality of the pareto-optimal sets of the proposed algorithms is determined by using the Hypervolume metric. This works considers two applications in which the MOSP problem occurs. The rst problem is the selection of optimal paths in the conventional vehicles and the second problem is the selection of optimal paths in the electric vehicles. The proposed algorithm outperforms the exist- ing single-solution-based EAs in solution quality and requires lesser memory than the population-based algorithms. The proposed algorithms can also be generalized to solve any multi-objective optimization problems. The proposed algorithm can solve complicated test problems of multi-objective optimization with a quality which is competitive to the existing popular EAs. The e ect of memory size on the solution quality is also studied. It is found that excessive increase in the memory size does not improve the solution quality. The exper- imental results show that the proposed StocE and GA based algorithms are highly suitable to solve the MOSP problem in embedded systems学位記番号：工博甲46

A reduced-uncertainty hybrid evolutionary algorithm for solving dynamic shortest-path routing problem

The need for effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a Reduced Uncertainty Based Hybrid Evolutionary Algorithm (RUBHEA) to solve Dynamic Shortest Path Routing Problem (DSPRP) effectively and rapidly. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A Complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle