A new multistrategy fusion-based slime mould algorithm

The standard Slime Mould Algorithm has problems such as falling into local optimal traps, slow convergence speed and low precision. In order to improve the performance of the algorithm, a new Multi-strategy Fusion based Slime Mould Algorithm (MFSMA) was proposed. In MFSMA, slime mould population was initialized with singer chaotic mapping and evenly distributed in search space, the global search ability was improved by alternating between short distance search and occasionally longer distance walk with Levy-flight mechanism, and a nonlinear convergence factor was proposed to balance the exploration ability and development ability of the algorithm. The proposed algorithm was able to find more precise parameters in various optimization problems than the conventional one. During the test phase, comparison test was conducted on MFSMA and other three test functions. The results indicated that the MFSMA had better global search ability, faster convergence speed and higher solving accuracy.