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Algorithms, Volume 7, Issue 2 (June 2014), Pages 188-275

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Editorial

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Open AccessEditorial Editorial: Special Issue on Matching under Preferences
Algorithms 2014, 7(2), 203-205; doi:10.3390/a7020203
Received: 14 March 2014 / Accepted: 14 March 2014 / Published: 8 April 2014
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Abstract This special issue of Algorithms is devoted to the study of matching problems involving ordinal preferences from the standpoint of algorithms and complexity. Full article
(This article belongs to the Special Issue Special Issue on Matching under Preferences)

Research

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Open AccessArticle Faster and Simpler Approximation of Stable Matchings
Algorithms 2014, 7(2), 189-202; doi:10.3390/a7020189
Received: 5 September 2013 / Revised: 22 March 2014 / Accepted: 27 March 2014 / Published: 4 April 2014
Cited by 5 | PDF Full-text (234 KB) | HTML Full-text | XML Full-text
Abstract
We give a 32 -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
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We give a 3 2 -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. Full article
(This article belongs to the Special Issue Special Issue on Matching under Preferences)
Open AccessArticle Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC
Algorithms 2014, 7(2), 206-228; doi:10.3390/a7020206
Received: 7 February 2014 / Revised: 8 April 2014 / Accepted: 25 April 2014 / Published: 5 May 2014
Cited by 1 | PDF Full-text (317 KB) | HTML Full-text | XML Full-text
Abstract
Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented
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Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
Open AccessArticle Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem
Algorithms 2014, 7(2), 229-242; doi:10.3390/a7020229
Received: 9 March 2014 / Revised: 5 May 2014 / Accepted: 5 May 2014 / Published: 13 May 2014
Cited by 3 | PDF Full-text (265 KB) | HTML Full-text | XML Full-text
Abstract
The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete
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The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
Figures

Open AccessArticle Efficient Algorithms for Subgraph Listing
Algorithms 2014, 7(2), 243-252; doi:10.3390/a7020243
Received: 6 March 2014 / Revised: 4 May 2014 / Accepted: 7 May 2014 / Published: 15 May 2014
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Abstract
Subgraph isomorphism is a fundamental problem in graph theory. In this paper we focus on listing subgraphs isomorphic to a given pattern graph. First, we look at the algorithm due to Chiba and Nishizeki for listing complete subgraphs of fixed size, and show
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Subgraph isomorphism is a fundamental problem in graph theory. In this paper we focus on listing subgraphs isomorphic to a given pattern graph. First, we look at the algorithm due to Chiba and Nishizeki for listing complete subgraphs of fixed size, and show that it cannot be extended to general subgraphs of fixed size. Then, we consider the algorithm due to Ga̧sieniec et al. for finding multiple witnesses of a Boolean matrix product, and use it to design a new output-sensitive algorithm for listing all triangles in a graph. As a corollary, we obtain an output-sensitive algorithm for listing subgraphs and induced subgraphs isomorphic to an arbitrary fixed pattern graph. Full article
Open AccessArticle A Faster Quick Search Algorithm
Algorithms 2014, 7(2), 253-275; doi:10.3390/a7020253
Received: 25 April 2014 / Revised: 30 May 2014 / Accepted: 4 June 2014 / Published: 23 June 2014
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Abstract
We present the FQS (faster quick search) algorithm, an improved variation of the quick search algorithm. The quick search (QS) exact pattern matching algorithm and its variants are among the fastest practical matching algorithms today. The FQS algorithm computes a statistically expected shift
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We present the FQS (faster quick search) algorithm, an improved variation of the quick search algorithm. The quick search (QS) exact pattern matching algorithm and its variants are among the fastest practical matching algorithms today. The FQS algorithm computes a statistically expected shift value, which allows maximal shifts and a smaller number of comparisons between the pattern and the text. Compared to the state-of-the-art QS variants of exact pattern matching algorithms, the proposed FQS algorithm is the fastest when lΣl ≤ 128, where lΣl is the alphabet size. FQS also has a competitive running time when lΣl > 128. Running on three practical text files, E. coli (lΣl  = 4), Bible (lΣl  = 63) and World192 (lΣl  = 94), FQS resulted in the best performance in practice. Our FQS algorithm will have important applications in the domain of genomic database searching, involving DNA or RNA sequence databases with four symbols Σ = {A, C, G, T(/U)} or protein databases with lΣl = 20. Full article

Other

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Open AccessCorrection Correction: Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts. Algorithms 2014, 7, 166–185
Algorithms 2014, 7(2), 188; doi:10.3390/a7020188
Received: 31 March 2014 / Accepted: 1 April 2014 / Published: 2 April 2014
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Abstract It has come to our attention that due to an error in producing the PDF version of the paper [1], doi:10.3390/a7010166, website: http://www.mdpi.com/1999-4893/7/1/166, Figures 1 and 9 are displayed incorrectly. [...] Full article

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