Differential Evolutionary (DE) Based Interactive Recoloring Based on YUV Based Edge Detection for Interior Design

Ramlan, Fitria Wulandari; Palakonda, Vikas; Mallipeddi, Rammohan

doi:10.1109/ictc46691.2019.8939816

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…Many scholars have recently applied and implemented metaheuristic techniques in the context of complex optimization problems [8,9]. Several metaheuristic algorithms Appl.…”

Section: Introductionmentioning

confidence: 99%

Unified Evolutionary Algorithm Framework for Hybrid Power Converter

et al. 2022

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A significant amount of the literature is focused on converters that supply the required voltage with low input-current ripple to electronic devices. A hybrid converter that combines boost and Cuk converters was developed recently. This hybrid converter achieved a relatively low input-current ripple based on earlier strategies. This paper proposes a new model that simulates a hybrid power converter system using a unified evolutionary algorithm (EA). As part of this paper, we present an improved framework for a hybrid power converter. Moreover, a unified EA is developed to incorporate differential evolution (DE) and genetic algorithm (GA) properties in order to analyze the proposed modified hybrid power converter. This research describes a modified hybrid power converter that optimizes the zero-ripple duty cycle (Dz) through the proposed algorithm for minimizing the input-current ripple. Based on our simulation results, comparing the proposed method with the baseline algorithm reveals that the proposed approach is significantly more efficient than the baseline algorithm and achieves the minimum input-current ripple in different gain values. In addition, we observe that the proposed algorithm performs better than the DE and GA algorithms in terms of obtaining low input-current ripple results. Therefore, the proposed hybrid algorithm is becoming more efficient with hybridization.

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“…Many scholars have recently applied and implemented metaheuristic techniques in the context of complex optimization problems [8,9]. Several metaheuristic algorithms Appl.…”

Section: Introductionmentioning

confidence: 99%