EOSMA: An Equilibrium Optimizer Slime Mould Algorithm for Engineering Design Problems

In this paper, we propose EOSMA, an equilibrium optimizer (EO)-guided slime mould algorithm (SMA), to improve efficiency by optimizing the search of SMAs. Firstly, the exploration and exploitation abilities of SMA are adapted to enhance the exploration in the early stage and the exploitation in the later stage. Secondly, the anisotropic search operator of SMA is replaced with the search operator of the equilibrium optimizer (EO) to guide search space of SMAs (EOSMA). Finally, the stochastic difference mutation operator is added to help the SMA algorithm escape from local optima after population location updates and to increase the diversity of the population at the late iteration. The performance of EOSMA is evaluated on CEC2019 and CEC2021 functions and nine engineering design problems. The results show that for the challenging CEC2019 functions, EOSMA significantly outperforms the 15 well-known comparison algorithms in terms of Mean and Min metrics. For the latest CEC2021 functions, EOSMA significantly outperforms IMODE, LSHADE, and LSHADE_cnEpSin in terms of Min metrics. In particular, for all nine engineering problems, EOSMA is able to find feasible solutions satisfying all constraints and significantly outperforms the advanced comparison algorithms such as SMA, EO, MPA, IGWO, AGPSO, MTDE in terms of solution accuracy, speed, and robustness.