Voratas Kachitvichyanukul

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This dissertation is a study on the use of swarm methods for optimization, and is divided into three main parts. In the first part, two novel swarm metaheuristic algorithms—named Survival Sub-swarms Adaptive Particle Swarm Optimization (SSS-APSO) and Survival Sub-swarms Adaptive Particle Swarm Optimization with velocity-line bouncing (SSS-APSO-vb)—are(More)
To develop grid scheduling algorithms, a high performance simulator is necessary since grid is an uncontrollable and unrepeatable environment. In this paper, a discrete event simulation library called HyperSim is used as extensible building blocks for grid scheduling simulator. The use of event graph model for the grid simulation are proposed. This model is(More)
Let M be the mode of the hypergeometric distribution, which is defined as the integer portion of {(k + l)(nI + l)/(nl + n2 + 2)). The inverse transformation method is used for M max(O, k n2) < 10, and algorithm HBPE is used for M max(O, K n2) I 10. The overall algorithm framework for HBPE is acceptance/rejection and is implemented via composition. Three(More)
Inducing correlation between estimators is a common way to try to reduce variance in simulation experiments. To induce the correlation between estimators, random variates are generated as functions of the same random-number streams. Although the optimal correlation induction occurs with the inverse transformation. The inverse can be quite slow compared to(More)
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This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization(More)