MoSSE: a novel hybrid multi-objective meta-heuristic algorithm for engineering design problems

Abstract

This paper introduces a novel hybrid optimization algorithm called MoSSE by combining the features of Multi-objective Spotted Hyena Optimizer (MOSHO), Salp Swarm Algorithm (SSA), and Emperor Penguin Optimizer (EPO). MoSSE uses MOSHO’s searching capabilities to effectively discover the search space, SSA’s leading and selection process to achieve the fittest global solution with quicker convergence technique, and EPO’s effective mover technique for better adjustment of the next solution. The algorithm is tested on ten IEEE CEC-9 standard test functions and compared with seven well-known multi-objective optimization algorithms according to their performance. The experimental results show that MoSSE provides highly competitive outcomes in terms of convergence speed, searchability, and accuracy. Statistical testing is also performed on IEEE CEC-9 test functions. Four performance metrics (i.e., Hypervolume, $$\Delta _p$$ , Spread, and Epsilon) are used to validate the searching capability of the proposed algorithm. MoSSE is further applied to welded beam, multi-disk clutch brake, pressure vessel, 25-bar truss design problems to test its effectiveness. The findings show the utility of the proposed algorithm to resolve the real-life complex multi-objective optimization problems.