Sine cosine particle swarm optimization algorithm for optimizing large scale issues

With the increasing number of large-scale problems, traditional hybrid algorithms are prone to falling into local optima, insufficient diversity, and low convergence accuracy, which urgently need to be solved. In order to improve the efficiency of solving such problems, an improved sine cosine algorithm was designed by introducing dynamic position correction and orthogonal crossover mechanism. And combined with particle swarm optimization algorithm, Sine Cosine particle swarm optimization algorithm is proposed. The results indicated that the average and standard deviation of the Shere benchmark test function for this method were both 0. The dynamic position correction and orthogonal crossover mechanism of this algorithm ensured fast acquisition of optimal fitness values and high convergence accuracy. For the Quartic benchmark test function, the average and standard deviation of the research algorithm were 3.48 × 10^-5 and 2.72 × 10^-5, respectively. Therefore, this method had the best performance, with good search ability and solution accuracy. In the application of robot path planning, this method achieves zero collisions, a path smoothness of 0.12 rad/m, an average planning time of 2.45 s, and an emergency obstacle avoidance success rate of 98.6%, significantly improving the efficiency and reliability of path planning in large-scale complex environments. This provides a relatively efficient solution for large-scale optimization problems and has a promoting effect on the application of intelligent optimization algorithms in the field of robotics.