• 한국기계가공학회지
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• 제16권1호
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• pp.63-69
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• 2017

The golden section search method is widely used to optimize a single design variable in many fields due to its superior advantages of search. In this paper, a new direct search method is proposed by modifying the search structure of the golden section search method; thus, it can be adapted in the optimization of a single design variable for the injection molding process. This proposed method is applied to determine an optimal gate position for the injection molding of a bezel of an automated teller machine for minimizing the injection pressure. Thus, an optimal gate position where the injection pressure is decreased by 4.5 MPa to that of the initial position was obtained with a small number of simulations. It is anticipated that the current proposed search method can be utilized as a practical tool for optimizing single variables for injection molding design.

• Journal of Power Electronics
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• 제18권3호
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• pp.841-852
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• 2018

This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.