Algorithms2014, 7(2), 189-202; doi:10.3390/a7020189 - published online 4 April 2014 Show/Hide Abstract
Abstract: We give a -approximation algorithm for finding stable matchings that runs in O(m) time. The previous most well-known algorithm, by McDermid, has the same approximation ratio but runs in O(n3/2m) time, where n denotes the number of people andm is the total length of the preference lists in a given instance. In addition, the algorithm and the analysis are much simpler. We also give the extension of the algorithm for computing stable many-to-many matchings.
Algorithms2014, 7(2), 188; doi:10.3390/a7020188 - published online 2 April 2014 Show/Hide Abstract
Abstract: It has come to our attention that due to an error in producing the PDF version of the paper , doi:10.3390/a7010166, website: http://www.mdpi.com/1999-4893/7/1/166, Figures 1 and 9 are displayed incorrectly.
Algorithms2014, 7(1), 186-187; doi:10.3390/a7010186 - published online 25 March 2014 Show/Hide Abstract
Abstract: This special issue of Algorithms is dedicated to approaches to biological sequence analysis that have algorithmic novelty and potential for fundamental impact in methods used for genome research.
Algorithms2014, 7(1), 166-185; doi:10.3390/a7010166 - published online 21 March 2014 Show/Hide Abstract
Abstract: Looking at articles or conference papers published since the turn of the century, Pareto optimization is the dominating assessment method for multi-objective nonlinear optimization problems. However, is it always the method of choice for real-world applications, where either more than four objectives have to be considered, or the same type of task is repeated again and again with only minor modifications, in an automated optimization or planning process? This paper presents a classification of application scenarios and compares the Pareto approach with an extended version of the weighted sum, called cascaded weighted sum, for the different scenarios. Its range of application within the field of multi-objective optimization is discussed as well as its strengths and weaknesses.
Algorithms2014, 7(1), 145-165; doi:10.3390/a7010145 - published online 17 March 2014 Show/Hide Abstract
Abstract: We study the scheduling problem for data collection from sensor nodes to the sink node in wireless sensor networks, also referred to as the convergecast problem. The convergecast problem in general network topology has been proven to be NP-hard. In this paper, we propose our heuristic algorithm (finding the minimum scheduling time for convergecast (FMSTC)) for general network topology and evaluate the performance by simulation. The results of the simulation showed that the number of time slots to reach the sink node decreased with an increase in the power. We compared the performance of the proposed algorithm to the optimal time slots in a linear network topology. The proposed algorithm for convergecast in a general network topology has 2.27 times more time slots than that of a linear network topology. To the best of our knowledge, the proposed method is the first attempt to apply the optimal algorithm in a linear network topology to a general network topology.