Slime mould algorithm ( SMA) is a new meta heuristic algorithm based on the oscillatory predatory behavior of slime mouldindividuals. Because of its simple principle,SMA has been applied to a variety of complex optimization problems. The basic SMA stillhas disadvantages such as slow rate of convergence,insufficient accuracy,and poor robustness when dealing with some more complexproblems. To overcome these shortcomings and improve the performance of the original algorithm,we propose an improved slime mouldalgorithm ( GTSMA)?that integrates sine chaotic mapping, t -distribution,and golden sine strategy. Firstly,the Sine chaotic sequence isintroduced to initialize the population and improve the diversity of the slime mould population during the initial iteration process of the algorithm. Secondly,in the process of updating the position of slime mould individuals,the degree of freedom parameter?
t is fused with thebasic SMA to increase the probability of the algorithm jumping out of local optima. Finally, by combining with the golden sinealgorithm,better slime mould individuals are selected to output the optimal solution. The benchmark test function and CEC2021 test setwere used to compare the test results of GTSMA with other algorithms. Experimental results show that GTSMA has better robustness,optimization accuracy and convergence performance than that of other algorithms during the test. Applying GTSMA to engineeringoptimization problems further validates its superiority in handling practical optimization problems.