Improved Slime Mould Algorithm with Fusion of Multiple Strategies and Engineering Application
Slime mould algorithm(SMA)is a new meta heuristic algorithm based on the oscillatory predatory behavior of slime mould individuals.Because of its simple principle,SMA has been applied to a variety of complex optimization problems.The basic SMA still has disadvantages such as slow rate of convergence,insufficient accuracy,and poor robustness when dealing with some more complex problems.To overcome these shortcomings and improve the performance of the original algorithm,we propose an improved slime mould algorithm(GTSMA)that integrates sine chaotic mapping,t-distribution,and golden sine strategy.Firstly,the Sine chaotic sequence is introduced to initialize the population and improve the diversity of the slime mould population during the initial iteration process of the al-gorithm.Secondly,in the process of updating the position of slime mould individuals,the degree of freedom parametertis fused with the basic SMA to increase the probability of the algorithm jumping out of local optima.Finally,by combining with the golden sine algorithm,better slime mould individuals are selected to output the optimal solution.The benchmark test function and CEC2021 test set were used to compare the test results of GTSMA with other algorithms.Experimental results show that GTSMA has better robustness,op-timization accuracy and convergence performance than that of other algorithms during the test.Applying GTSMA to engineering optimization problems further validates its superiority in handling practical optimization problems.
slime mould algorithmSine chaotic mapadaptive t distributiongolden sine algorithmengineering optimization problem
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