Özet:

When looking to the real-world problems, it is seen that many of them are aimed at achieving more than one goal. The deficiency of the classical methods at the point of developing solutions due to different reasons has led researchers to develop different approaches. Nature-inspired algorithms developed by taking inspiration from the behavior of animals that generally live with a swarm in nature or plants with different habitats have been one of these approaches. In this study, shuffled frog leaping (SFLA) and gray wolf optimizer (GWO) algorithms, which developed for solving single-objective problems, are applied to multi-objective optimization problems in a hybrid manner. The proposed algorithm has been applied on some multi-objective engineering design and multi-objective constrained problems. The performance of the proposed algorithm has been compared with the performance of NSGA-II, IBEA, MOCell and PAES algorithms. HV, IGD, Spread and Epsilon metrics are used as performance comparison metrics. Performance analysis was performed using the average results obtained, Friedman ranking test and Wilcoxon significance test. Experimental results have shown that the proposed algorithm generates more successful results than other algorithms.

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