8 research outputs found
Quick Combinatorial Artificial Bee Colony -qCABC- Optimization Algorithm for TSP
Combinatorial Artificial Bee Colony Algorithm (CABC) is a new version of Artificial Bee Colony (ABC) to solve combinatorial type optimization problems and quick Artificial Bee Colony (qABC) algorithm is an improved version of ABC in which the onlooker bees behavior is modeled in more detailed way. Studies showed that qABC algorithm improves the convergence performance of standard ABC on numerical optimization. In this paper, to see the performance of this new modeling way of onlookers' behavior on combinatorial optimization, we apply the qABC idea to CABC and name this new algorithm as quick CABC (qCABC). qCABC is tested on Traveling Salesman Problem and simulation results show that qCABC algorithm improves the convergence and final performance of CABC
Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps
This is the final version. Available from SPIE via the DOI in this record.Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, 10 - 13 September 2018, Berlin, GermanyUnmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed,
automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having
increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood
monitoring and prevention strategies are required. The limited battery life of small lightweight UAVs imposes
efficient strategies to subsample the sensing field. This paper proposes a novel solution to maximise the number of
inspected flooded cells while keeping the travelled distance bounded. Our proposal solves the so-called continuous
Travelling Salesman Problem (TSP), where the costs of travelling from one cell to another depend not only on
the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ
the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded
regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered
flooded surface approximately 4 times greater for the same travelled distance when compared to the conventional
TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to
monitor water resourcesThis work was funded by the Royal Society of Edinburgh and National Science Foundation of China within the
international project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned
Aerial Vehicles” (project NNS/INT 15-16 Casaseca)
Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation
A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence
Traveling Salesman Problem
The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance
Traveling Salesman Problem
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering