Enhancing Sine-Cosine mutation strategy with Lorentz distribution for solving engineering design problems

To solve global optimization, this paper proposes an improved sine-cosine algorithm to address the limitation of the basic sine cosine algorithm problems such as low solution precision and sluggish convergent speed. To overcome this weakness and to increase its search capabilities, two strategies were involved. Firstly, exponential decreasing conversion parameter which is used to balance the global exploration and local search ability of the algorithm. Secondly the Lorentz search strategy to generate new candidate individual and the capacity to avoid early convergence to effectively explore the search space. Sine Cosine Algorithm is developed to solve difficult problems, implying it has a higher accuracy and convergence rate based on the position updating equations incorporation of the objective function component and the trigonometric function term. Sometimes the search path does not search towards the global best and the result obtained is only a local optimum when solving multi-parameter optimization and highly ill-conditioned problems. Therefore, in the present study new method called Lorentz-SCA is introduced, which tries to alleviate all these problems. The suggested proposed algorithm has been put to the test against a standard set of 23 well-known benchmark functions and 12 highly non -linear engineering design problems to test the effectiveness of the design algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.