A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems

Multi-objective optimization is many important since most of the real world problems are in multi-objective category. Looking at the literature, the algorithms proposed for the solution of multi-objective problems have increased in recent years, but there is no a convenient approach for all kind of problems. Therefore, researchers aim to contribute to the literature by offering new approaches. In this study, an algorithm based on gray wolf optimizer (GWO) with memeplex structure of the shuffled frog leaping algorithm (SFLA), which is named as multi-objective shuffled GWO (MOSG), is proposed to solve the multi-objective optimization problems. Additionally, some modifications are applied on the proposed algorithm to improve the performance from different angles. The performance of the proposed algorithm is compared with the performance of six multi-objective algorithms on a benchmark set consist of 36 problems. The experimental results are presented with four different comparison metrics and statistical tests. According to the results, it can easily be said that the proposed algorithm is generally successful to solve the multi-objective problems and has better or competitive results. [Display omitted] •A novel algorithm named as MOSG has been proposed to solve multi-objective problems.•The MOSG has been tested on 36 different benchmark problems along with 6 other algorithms.•HV, IGD, Spread and Epsilon have been used as the performance metrics.•Experimental results have been presented by using statistical methods.•Experiments have showed that the MOSG is a quite successful and competitive algorithm.