Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems

Slime Mould Algorithm (SMA) is a recently introduced meta-heuristic stochastic method, which simulates the bio-oscillator of slime mould. In this paper, an improved variant of SMA is proposed, called OBLSMAL, to relieve the conventional method’s main weaknesses that converge fast/slow and fall in the local optima trap when dealing with complex and high dimensional problems. Two search strategies are added to conventional SMA. Firstly, opposition-based learning (OBL) is employed to improve the convergence speed of the SMA. Secondly, the Levy flight distribution (LFD) is used to enhance the ability of the exploration and exploitation searches during the early and later stages, respectively. The integrated two search methods significantly improve the convergence behavior and the searchability of the conventional SMA. The performance of the proposed OBLSMAL method is comprehensively investigated and analyzed by using (1) twenty-three classical benchmark functions such as unimodal, multi-modal, and fixed multi-modal, (2) ten IEEE CEC2019 benchmark functions, and (3) five common engineering design problems. The experimental results demonstrate that the search strategies of SMA and its convergence behavior are significantly developed. The proposed OBLSMAL achieves promising results, and it gets better performance compared to other well-known optimization methods.