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The job-shop scheduling problem has attracted many researchers' attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm(More)
Keywords: Forecasting Two-factors Fuzzy relationships Fuzzy time series Modify turbulent particle swarm optimization a b s t r a c t In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series(More)
Most fuzzy forecasting approaches are based on modeling fuzzy relations according to the past data. In this paper, an improved forecasting model which combines weighted fuzzy relationship matrices and particle swarm optimization is presented for enrollments. First, the weighted fuzzy relationship matrices are more effective to capture fuzzy relations on(More)
ODGOMS is a multi-strategy ontology matching system which consists of elemental level, structural level, and optimization level strategies. When it starts to match ontologies, it first exploits appropriate string-based and token-based similarity computing strategies to find preliminary aligned results, and then it filters these results and merges them by(More)