Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems

The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents' local search, increase the population's diversity, and help avoid premature convergence. The SMA-AGDE's performance is evaluated on the CEC'17 test suite, three engineering design problems - tension/compression spring, pressure vessel, and rolling element bearing - and two combinatorial optimization problems - bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The wellstudied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.

Automated summary

The study introduces a hybrid algorithm SMA-AGDE, which combines SMA and AGDE to enhance global search and population diversity, achieving superior performance in multiple test cases.