Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization

Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BT beta SMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive beta-hill climbing, A beta HC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BT beta SMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BT beta SMA-1 and BT beta SMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BT beta SMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BT beta SMA algorithm in terms of solution precision.

Automated summary

The study proposes an improved Slime mould algorithm and its hybrid optimization algorithm to improve the quality of optimal results, while also enhancing exploration and exploitation capability.