Ensemble of four metaheuristic using a weighted sum technique for aircraft wing design
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Abstract
Recently, metaheuristics (MHs) have become increasingly popular in real-world engineering applications such as in the design of airframes structures and aeroelastic designs owing to its simple, flexible, and efficient nature. In this study, a novel hybrid algorithm is termed as Ensemble of Genetic algorithm, Grey wolf optimizer, Water cycle algorithm and Population base increment learning using Weighted sum (E-GGWP-W), based on the successive archive methodology of the weighted population has been proposed to solve the aircraft composite wing design problem. Four distinguished algorithms viz. a Genetic algorithm (GA), a Grey wolf optimizer (GWO), a Water cycle algorithm (WCA), and Population base increment learning (PBIL) were used as ingredients of the proposed algorithm. The considered wing design problem is posed for overall weight minimization subject to several aeroelastic and structural constraints along with multiple discrete design variables to ascertain its viability for real-world applications. The algorithms are validated through the standard test functions of the CEC-RW-2020 test suite and composite Goland wing aeroelastic optimization. To check the performance, the proposed algorithm is contrasted with eight well established and newly developed MHs. Finally, a statistical analysis is done by performing Friedman’s rank test and allocating respective ranks to the algorithms. Based on the outcome, it has been observed that the suggested algorithm outperforms the others.
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