Multi Strategy Improvement of Newton-Raphson-Based Optimizer and Engineering Application

A multi strategy improved Newton- Raphson-Based Optimizer (NRBO) is proposed to address the problem of slow convergence speed in the later stage of algorithm iteration and difficulty in finding the global optimal solution when facing complex functions. Firstly, using a set of best points instead of randomly generated initial populations to evenly distribute individuals in the optimization space improves the efficiency of the algorithms global and local search. Secondly, based on the idea of differential evolution, the initial population is mutated and crossed to accelerate the iteration speed. Then, in the optimal update stage, the Levi flight strategy is introduced to enhance the algorithms ability to jump out of local optima. The effectiveness of the proposed algorithm was tested on the CEC2005 test set, and compared with other algorithms on two test sets. The results showed that the convergence speed and accuracy of the algorithm were significantly improved. Finally, the proposed algorithm was applied to two practical engineering optimization problems, and simulation results showed that the algorithm proposed in this paper has better practicality and robustness compared to other algorithms.