Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

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Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics

This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising metaheuristics for solving the single-source shortest-path problem (SPP) commonly encountered in graph theory. This hybrid search process combines PSO for iteratively finding a population of better solutions and noising method for diversifying the search scheme to solve this problem. A new encoding/decoding scheme based on heuristics has been devised for representing the SPP parameters as a particle in PSO. Noising-method-based metaheuristics ( noisy local search) have been incorporated in order to enhance the overall search effciency. In particular, an iteration of the proposed hybrid algorithm consists of a standard PSO iteration and few trials of noising scheme applied to each better/improved particle for local search, where the neighborhood of each such particle is noisily explored with an elementary transformation of the particle so as to escape possible local minima and to diversify the search. Simulation results on several networks with random topologies are used to illustrate the effciency of the proposed hybrid algorithm for shortest-path computation. The proposed algorithm can be used as a platform for solving other NP-hard SPPs. Copyright (c) 2007 A. W. Mohemmed and N. C. Sahoo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Particle Swarm Optimization Combined with Local Search and Velocity Re-Initialization for Shortest Path Computation in Networks

This paper presents the application of particle swarm optimization (PSO) based search algorithm for solving the single source shortest path problem (SPP) commonly encountered in graph theory. A new particle encoding/decoding scheme has been devised for representing the SPP parameters as a particle. In order to enhance the search capability of PSO, a selective local search mechanism and periodic velocity re-initialization of particles have been incorporated. Simulation results on several networks with random topologies are used to illustrate the efficiency of the proposed hybrid PSO algorithm for computation of shortest paths in networks

Particle swarm optimisation for outlier detection

Outlier detection is an important problem as the underlying data points often contain crucial information, but identifying such points has multiple challenges, e.g. noisy data, imprecise boundaries and lack of training examples. In the novel approach presented in this paper, the outlier detection problem is converted into an optimisation problem. A Particle Swarm Optimisation (PSO) based approach to outlier detection is then applied, which expands the scope of PSO and enables new insights into outlier detection. Namely, PSO is used to automatically optimise the key distance measures instead of manually setting the distance parameters via trial and error, which is inefficient and often ineffective. The novel PSO approach is examined and compared with a commonly used detection method, Local Outlier Factor (LOF), on five real data sets. The results show that the new PSO method significantly outperforms the LOF methods for correctly detecting the outliers on the majority of the datasets and that the new PSO method is more efficient than the LOF method on the datasets tested.</p

A new particle swarm optimization based algorithm for solving shortest-paths tree problem

This paper presents an efficient particle swarm optimization (PSO) based search algorithm for solving the single source all destination shortest paths or what is called the shortest-paths tree (SPT), commonly encountered in graph theory. A new particle encoding/decoding scheme has been devised for particle-representation of the SPT parameters. This encoding/decoding exploits the sub-optimality feature of the shortest path. In the proposed algorithm, the solution, the shortest path tree, is not represented by one particle, but it is the solution that is contributed by the complete swarm population. Numerical computation results on several networks with random topologies illustrate the efficiency of the proposed PSO method for computation of shortest paths in networks

Particle swarm optimization with noising metaheuristics for solving network shortest path problem

This paper presents an efficient particle swarm optimization (PSO) based search algorithm for solving the single source shortest path problem (SPP), commonly encountered in graph theory. A particle encoding/decoding scheme has been devised for particle-representation of the SPP parameters. The search capability of PSO is diversified by hybridizing the PSO with a noising metaheuristics. Numerical computation results on several networks with random topologies illustrate the efficiency of the proposed hybrid PSO-Noising method for computation of shortest paths in networks

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15-70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem. (c) 2008 Elsevier B.V. All rights reserved

A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram

The coverage problem is a crucial issue in wireless sensor networks (WSN), where a high coverage rate ensures a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO) and Voronoi diagram. PSO is used to rind the optimal deployment of the sensors that gives the best coverage while Voronoi diagram is used to evaluate the fitness of the solution. The algorithm is evaluated through simulation in different WSN. The simulation results show that the proposed algorithm achieves a good coverage with a better time efficiency

Particle swarm optimization and Voronoi Diagram for wireless sensor networks coverage optimization

The focus of this study is the sensor coverage problem. It is a crucial issue in Wireless Sensor Networks (WSN), where a high coverage rate will ensure a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using Particle Swarm Optimization (PSO). PSO is chosen to find the optimal position of the sensors that gives the best coverage and Voronoi diagram is used to evaluate the fitness of the solution