Improved Swarm Intelligence Algorithm Based on Novel Nonlinear Multi-Strategy Optimisation

Aiming at the problems of low solution accuracy of the grey wolf optimization algorithm and the tendency to fall into local optimality in the later stage, an improved swarm intelligence algorithm NSGWO based on a novel nonlinear multi-strategy optimization is proposed: firstly, Logistic mapping is utilized to make the initial particles as uniformly distributed as possible, so as to provide a balanced search space improved Gaussian distributions are used to adjust the convergence factor, and a position in the NSGWO The logarithmic function is introduced into the updating formula. Finally, an optimal position perturbation mechanism is introduced, which prompts the algorithm to jump out of the local optimal solution by perturbing the optimal position in a small range, thus further improving the optimisation performance of the algorithm. Several classical unimodal and multimodal test functions are used to verify the optimisation performance of NSGWO. The experimental results show that compared with other classical optimisation algorithms, NSGWO has a greater advantage in terms of convergence speed and solution accuracy.