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Higashimurayama-shi, Japan

Hannan University is a private university in Matsubara, Osaka, Japan. It was founded in 1965. Wikipedia.

Tsutsui S.,Hannan University
IEEJ Transactions on Electronics, Information and Systems | Year: 2013

Recently, GPGPU (General Purpose computation on Graphics Processing Units) has become popular with great success, especially in scientific fields such as fluid dynamics, image processing, and visualization using particle methods. In this paper, we discuss how the GPGPU is used in implementations of parallel ant colony optimization (ACO) for fast solution of quadratic assignment problems (QAPs). As for the ACO, we use the cunning ant system (cAS) which is one of the most promising ACO algorithms. © 2013 The Institute of Electrical Engineers of Japan. Source

Tsutsui S.,Hannan University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In this paper, we implement ACO algorithms on a PC which has 4 GTX 480 GPUs. We implement two types of ACO models; the island model, and the master/slave model. When we compare the island model and the master/slave model, the island model shows promising speedup values on class (iv) QAP instances. On the other hand, the master/slave model showed promising speedup values on both classes (i) and (iv) with large-size QAP instances. © 2012 Springer-Verlag. Source

After land reallocation in the early 1980s, inequality in landholdings has re-emerged in rural Cambodia. Besides land sales and purchases, intergenerational transfers of assets may foster inequality in landholdings among "second generation" (2G) couples who, having wed after the 1980s reallocation, received no land from the government. Data analysis of three rice-growing villages reveals that land received directly from parents accounts for 18-41% of inequality in landholdings among sample 2G couples. Although net land gain after marriage, primarily through purchases, is the largest contributor to the inequality, nonland assets received from parents positively affect the net gain. Direct and indirect effects combined, assets received from parents account for 35-57% of inequality in landholdings. The effect of assortative matching of the acreage received from parents has hitherto been small. © 2015 International Association of Agricultural Economists. Source

Fujimoto N.,Osaka Prefecture University | Tsutsui S.,Hannan University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

The traveling salesman problem (TSP) is probably the most widely studied combinatorial optimization problem and has become a standard testbed for new algorithmic ideas. Recently the use of a GPU (Graphics Processing Unit) to accelerate non-graphics computations has attracted much attention due to its high performance and low cost. This paper presents a novel method to solve TSP with a GPU based on the CUDA architecture. The proposed method highly parallelizes a serial metaheuristic algorithm which is a genetic algorithm with the OX (order crossover) operator and the 2-opt local search. The experiments with an NVIDIA GeForce GTX285 GPU and a single core of 3.0 GHz Intel Core2 Duo E6850 CPU show that our GPU implementation is about up to 24.2 times faster than the corresponding CPU implementation. © 2011 Springer-Verlag. Source

Tsutsui S.,Hannan University | Fujimoto N.,Osaka Prefecture University
Genetic and Evolutionary Computation Conference, GECCO'11 | Year: 2011

This paper proposes an ant colony optimization (ACO) for solving quadratic assignment problems (QAPs) on a graphics processing unit (GPU) by combining tabu (TS) with ACO in CUDA (ompute unified device architecture). In TS for QAP, all neighbor moves are tested. These moves form two groups based on computing of move cost. In one group, the computing of cost is O(1) and in the other group, the computing of move cost is O(n). We compute these two groups of moves in parallel by assigning the computations to threads of CUDA. In this assignment, we propose an efficient method which we call Move-Cost Adjusted Thread Assignment (MATA). The results with GPU computation with MATA show a promising speedup compared to computation with the CPU. It is also shown that MATA is effective in applying 2-opt local search. Copyright 2011 ACM. Source

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