Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Abstract The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of populations. Although it has few parameters easy to implement, suffers from lack exploitation, leading slow convergence. This paper proposes an improved EHO called manta ray foraging Gaussian mutation-based for global (MGEHO). updating operator in original replaced somersault strategy rays, which aims optimally adjust patriarch positions. Additionally, dynamic convergence factor set balance exploration exploitation. gaussian mutation adopted enhance population diversity, enabling MGEHO maintain strong local search capability. To evaluate performances different algorithms, 33 classical benchmark functions are chosen verify superiority MGEHO. Also, enhanced paradigm compared with other advanced algorithms on 32 IEEE CEC2014 CEC2017. Furthermore, scalability test, analysis, statistical diversity running time analysis demonstrate effectiveness various aspects. results illustrate that superior terms solution accuracy stability. Finally, applied solve three real engineering problems. comparison show this method powerful auxiliary tool handling complex