The social learning process of birds and fishesinspired the development of the heuristic Particle Swarm Optimization (PSO) search algorithm. The advancement of GraphicsProcessing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform plays a significant role to reduce thecomputational time in search algorithm development. This paperpresents a good implementation for the Standard Particle SwarmOptimization (SPSO) on a GPU based on the CUDA architecture, which uses coalescing memory access. The algorithm is evaluatedon a suite of well-known benchmark optimization functions. Theexperiments are performed on an NVIDIA GeForce GTX 980GPU and a single core of 3.20 GHz Intel Core i5 4570 CPUand the test results demonstrate that the GPU algorithm runsabout maximum 46 times faster than the corresponding CPUalgorithm. Therefore, this proposed algorithm can be used toimprove required time to solve optimization problems.
{"title":"A CUDA Implementation of the Standard Particle Swarm Optimization","authors":"M. M. Hussain, H. Hattori, N. Fujimoto","doi":"10.1109/SYNASC.2016.043","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.043","url":null,"abstract":"The social learning process of birds and fishesinspired the development of the heuristic Particle Swarm Optimization (PSO) search algorithm. The advancement of GraphicsProcessing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform plays a significant role to reduce thecomputational time in search algorithm development. This paperpresents a good implementation for the Standard Particle SwarmOptimization (SPSO) on a GPU based on the CUDA architecture, which uses coalescing memory access. The algorithm is evaluatedon a suite of well-known benchmark optimization functions. Theexperiments are performed on an NVIDIA GeForce GTX 980GPU and a single core of 3.20 GHz Intel Core i5 4570 CPUand the test results demonstrate that the GPU algorithm runsabout maximum 46 times faster than the corresponding CPUalgorithm. Therefore, this proposed algorithm can be used toimprove required time to solve optimization problems.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127040174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Chifu, I. Salomie, Laura Petrisor, E. Chifu, Dorin Moldovan
This paper presents a Hybrid Clonal Selection based method for generating healthy meals as starting from a given user request, a diet recommendation, and a set of food offers. The method proposed is based on a hybrid model, which consists of one core component and two hybridization components. The core component uses the CLONAG algorithm. One of the hybridization components is based on flower pollination, whereas the other utilizes tabu search and reinforcement learning. The flower pollination component is used for modifying the generated clones, while the tabu search and reinforcement learning component aims to improve the search capabilities of the core component by means of long-term and short-term memory structures. We integrated our method into an experimental prototype and we evaluated it on different older adult profiles.
{"title":"Hybrid Immune Based Method for Generating Healthy Meals for Older Adults","authors":"V. Chifu, I. Salomie, Laura Petrisor, E. Chifu, Dorin Moldovan","doi":"10.1109/SYNASC.2016.047","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.047","url":null,"abstract":"This paper presents a Hybrid Clonal Selection based method for generating healthy meals as starting from a given user request, a diet recommendation, and a set of food offers. The method proposed is based on a hybrid model, which consists of one core component and two hybridization components. The core component uses the CLONAG algorithm. One of the hybridization components is based on flower pollination, whereas the other utilizes tabu search and reinforcement learning. The flower pollination component is used for modifying the generated clones, while the tabu search and reinforcement learning component aims to improve the search capabilities of the core component by means of long-term and short-term memory structures. We integrated our method into an experimental prototype and we evaluated it on different older adult profiles.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131938350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Irrelevance, a notion which was first put forward by this author jointly with A. Sgarro, is a convenient tool to speed up computations in the arithmetic of interactive fuzzy numbers. In this paper we are trying to understand what happens if the fuzzy quantities one is considering are incomplete, or sub-normal, that is if one allows that a fuzzy quantity is "cut" at a height h which is less than 1. We motivate the reasons why we deem it important to extend fuzzy arithmetic to fuzzy quantities which may be incomplete, and we show that irrelevance keeps proving a convenient tool. Interactivity is described by suitable monotone joins, which generalize t-norms.
{"title":"Irrelevance in Incomplete Fuzzy Arithmetic","authors":"Laura Franzoi","doi":"10.1109/SYNASC.2016.052","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.052","url":null,"abstract":"Irrelevance, a notion which was first put forward by this author jointly with A. Sgarro, is a convenient tool to speed up computations in the arithmetic of interactive fuzzy numbers. In this paper we are trying to understand what happens if the fuzzy quantities one is considering are incomplete, or sub-normal, that is if one allows that a fuzzy quantity is \"cut\" at a height h which is less than 1. We motivate the reasons why we deem it important to extend fuzzy arithmetic to fuzzy quantities which may be incomplete, and we show that irrelevance keeps proving a convenient tool. Interactivity is described by suitable monotone joins, which generalize t-norms.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates a novel update rule formulti–state Cellular Automata (CA) in the context of greyscaleimage segmentation. The update rule is parameterized and takesinto account the features of neighbouring cells compared to thefeatures of the current cell. We use the resulting CA to segmentseveral real–world images. During this process we also studythe influence of the rule parameters and neighbourhood schemeusing different evaluation measures.
{"title":"Parameterized Cellular Automata in Image Segmentation","authors":"A. Andreica, L. Dioşan, I. Voiculescu","doi":"10.1109/SYNASC.2016.040","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.040","url":null,"abstract":"This paper investigates a novel update rule formulti–state Cellular Automata (CA) in the context of greyscaleimage segmentation. The update rule is parameterized and takesinto account the features of neighbouring cells compared to thefeatures of the current cell. We use the resulting CA to segmentseveral real–world images. During this process we also studythe influence of the rule parameters and neighbourhood schemeusing different evaluation measures.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"66 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123187919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerical reproducibility failures rise in parallel computation because of the non-associativity of floating-point summation. Optimizations on massively parallel systems dynamically modify the floating-point operation order. Hence, numerical results may change from one run to another. We propose to ensure reproducibility by extending as far as possible the IEEE-754 correct rounding property to larger operation sequences. Our RARE-BLAS (Reproducible, Accurately Rounded and Efficient BLAS) benefits from recent accurate and efficient summation algorithms. Solutions for level 1 (asum, dot and nrm2) and level 2 (gemv) routines are provided. We compare their performance to the Intel MKL library and to other existing reproducible algorithms. For both shared and distributed memory parallel systems, we exhibit an extra-cost of 2× in the worst case scenario, which is satisfying for a wide range of applications. For Intel Xeon Phi accelerator a larger extra-cost (4× to 6×) is observed, which is still helpful at least for debugging and validation.
{"title":"Parallel Experiments with RARE-BLAS","authors":"Chemseddine Chohra, P. Langlois, David Parello","doi":"10.1109/SYNASC.2016.032","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.032","url":null,"abstract":"Numerical reproducibility failures rise in parallel computation because of the non-associativity of floating-point summation. Optimizations on massively parallel systems dynamically modify the floating-point operation order. Hence, numerical results may change from one run to another. We propose to ensure reproducibility by extending as far as possible the IEEE-754 correct rounding property to larger operation sequences. Our RARE-BLAS (Reproducible, Accurately Rounded and Efficient BLAS) benefits from recent accurate and efficient summation algorithms. Solutions for level 1 (asum, dot and nrm2) and level 2 (gemv) routines are provided. We compare their performance to the Intel MKL library and to other existing reproducible algorithms. For both shared and distributed memory parallel systems, we exhibit an extra-cost of 2× in the worst case scenario, which is satisfying for a wide range of applications. For Intel Xeon Phi accelerator a larger extra-cost (4× to 6×) is observed, which is still helpful at least for debugging and validation.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liviu Octavian Mafteiu-Scai, Calin Alexandru Cornigeanu
This paper proposes a parallel hybrid heuristic aiming the reduction of the bandwidth of sparse matrices. Mainly based on the geometry of the matrix, the proposed method uses a greedy selection of rows/columns to be interchanged, depending on the nonzero extremities and other parameters of the matrix. Experimental results obtained on an IBM Blue Gene/P supercomputer illustrate the fact that the proposed parallel heuristic leads to better results, with respect to time efficiency, speedup, efficiency and quality of solution, in comparison with serial variants and of course in comparison with other reported results.
提出了一种以减少稀疏矩阵带宽为目标的并行混合启发式算法。该方法主要基于矩阵的几何特性，根据矩阵的非零极值和其他参数，贪婪地选择待交换的行/列。在IBM Blue Gene/P超级计算机上获得的实验结果表明，与串行变量相比，当然也与其他报告的结果相比，所提出的并行启发式在时间效率、加速、效率和解决方案质量方面都有更好的结果。
{"title":"A Parallel Heuristic for Bandwidth Reduction Based on Matrix Geometry","authors":"Liviu Octavian Mafteiu-Scai, Calin Alexandru Cornigeanu","doi":"10.1109/SYNASC.2016.071","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.071","url":null,"abstract":"This paper proposes a parallel hybrid heuristic aiming the reduction of the bandwidth of sparse matrices. Mainly based on the geometry of the matrix, the proposed method uses a greedy selection of rows/columns to be interchanged, depending on the nonzero extremities and other parameters of the matrix. Experimental results obtained on an IBM Blue Gene/P supercomputer illustrate the fact that the proposed parallel heuristic leads to better results, with respect to time efficiency, speedup, efficiency and quality of solution, in comparison with serial variants and of course in comparison with other reported results.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce Venn diagrams for multisets and showhow they simplify the analysis of multisets. Venn diagrams arevery useful in proofs involving multisets and multiset orders, especially considering the complications introduced by the multiplicity of elements in multisets. We compare the Venn diagramsfor multisets with the corresponding ones for sets. Thus, wepresent two types of Venn diagrams for multisets, a simple onethat looks like a diagram for sets, but with areas that are notnecessarily disjoint, and a complex one (compared to sets), butwith certain delimited disjoint areas. We determine the numberof non-composite areas (disjoint or not) in a Venn diagram formultisets, for which we give two sequences of integers. We compare several properties of Venn diagrams for sets and multisets, like symmetry and Hamiltonicity. Venn diagrams for multisetscan also be used for databases, knowledge representation systems, in artificial intelligence, Semantic Web.
{"title":"Venn Diagrams for Multisets","authors":"Aurelian Radoaca","doi":"10.1109/SYNASC.2016.039","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.039","url":null,"abstract":"We introduce Venn diagrams for multisets and showhow they simplify the analysis of multisets. Venn diagrams arevery useful in proofs involving multisets and multiset orders, especially considering the complications introduced by the multiplicity of elements in multisets. We compare the Venn diagramsfor multisets with the corresponding ones for sets. Thus, wepresent two types of Venn diagrams for multisets, a simple onethat looks like a diagram for sets, but with areas that are notnecessarily disjoint, and a complex one (compared to sets), butwith certain delimited disjoint areas. We determine the numberof non-composite areas (disjoint or not) in a Venn diagram formultisets, for which we give two sequences of integers. We compare several properties of Venn diagrams for sets and multisets, like symmetry and Hamiltonicity. Venn diagrams for multisetscan also be used for databases, knowledge representation systems, in artificial intelligence, Semantic Web.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a hybrid test generation approach from extended finite state machines combining genetic algorithms with local search techniques. Many test generation methods (both functional and structural testing methods) use genetic algorithms. Genetic algorithms may take a long time to converge to a global optimum and for a huge neighborhood they can be inefficient or unsuccessful. In this paper we use hybrid genetic algorithms to generate test data for some chosen paths for extended finite state machines. Local search is applied to improve the best individual for each generation of the genetic algorithm.
{"title":"A Hybrid Test Generation Approach Based on Extended Finite State Machines","authors":"Ana Turlea, F. Ipate, R. Lefticaru","doi":"10.1109/SYNASC.2016.037","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.037","url":null,"abstract":"This paper presents a hybrid test generation approach from extended finite state machines combining genetic algorithms with local search techniques. Many test generation methods (both functional and structural testing methods) use genetic algorithms. Genetic algorithms may take a long time to converge to a global optimum and for a huge neighborhood they can be inefficient or unsuccessful. In this paper we use hybrid genetic algorithms to generate test data for some chosen paths for extended finite state machines. Local search is applied to improve the best individual for each generation of the genetic algorithm.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127308606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The extended Hensel construction (EHC) is a direct extension of the generalized Hensel construction (GHC), and it targets sparse multivariate polynomials for which the GHC breaks down. The EHC consists of two Hensel constructions which we call separation of "maximal" and "minimal" Hensel factors (see the text). As for the minimal Hensel factor separation, very recently, we enhanced the old algorithm largely by using Groebner basis of two initial factors and syzygies for the elements of the basis. In this paper, we first improve the old algorithm for maximal Hensel factors. We then enhance further the Groebner basis computation in our recent algorithm. The latter is based on a theoretical analysis of the Groebner bases. Simple experiments show that the improved part for the minimal Hensel factors is much faster than the recent one.
{"title":"Various Enhancements for Extended Hensel Construction of Sparse Multivariate Polynomials","authors":"Tateaki Sasaki, D. Inaba","doi":"10.1109/SYNASC.2016.025","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.025","url":null,"abstract":"The extended Hensel construction (EHC) is a direct extension of the generalized Hensel construction (GHC), and it targets sparse multivariate polynomials for which the GHC breaks down. The EHC consists of two Hensel constructions which we call separation of \"maximal\" and \"minimal\" Hensel factors (see the text). As for the minimal Hensel factor separation, very recently, we enhanced the old algorithm largely by using Groebner basis of two initial factors and syzygies for the elements of the basis. In this paper, we first improve the old algorithm for maximal Hensel factors. We then enhance further the Groebner basis computation in our recent algorithm. The latter is based on a theoretical analysis of the Groebner bases. Simple experiments show that the improved part for the minimal Hensel factors is much faster than the recent one.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"52 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hybrid sets are generalizations of sets and multisets, in which the multiplicities of elements can take any integers. This construction was proposed by Whitney in 1933 in terms of characteristic functions. Hybrid sets have been used by combinatorists to give combinatorial interpretationsfor several generalizations of binomial coefficients and Stirling numbers and by computer scientists to design fast algorithms for symbolic domain decompositions. We present in this paper some combinatorial results on subsets and partitions of hybrid sets.
{"title":"Combinatorics of Hybrid Sets","authors":"Shaoshi Chen, S. Watt","doi":"10.1109/SYNASC.2016.022","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.022","url":null,"abstract":"Hybrid sets are generalizations of sets and multisets, in which the multiplicities of elements can take any integers. This construction was proposed by Whitney in 1933 in terms of characteristic functions. Hybrid sets have been used by combinatorists to give combinatorial interpretationsfor several generalizations of binomial coefficients and Stirling numbers and by computer scientists to design fast algorithms for symbolic domain decompositions. We present in this paper some combinatorial results on subsets and partitions of hybrid sets.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125978524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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