Resumen de A Hybrid Computational Intelligence Method of Newton's Method andGeneticAlgorithm for Solving Compatible Nonlinear Equations
Yanfeng Wang, Baocheng Yang, Pengrui Chen, Xue Zhang, Guanning Ma, Bintao Yuan, Ayman Al dmour
In order to solve the system of compatible nonlinear equations, the author proposes a hybrid computational intelligence method of Newton's method and genetic algorithm. First, the Quasi-Newton Methods (QN) method is given. Aiming at the local convergence of the algorithm, it is easy to cause the solution to fail. By embedding the QN operator in the Genetic Algorithm (GA) and defining the appropriate fitness, thus, ahybrid computational intelligence algorithm of CNLE is obtained that combines the advantages of GAandQN method, which has both faster convergence and higher probability of solving. Experimental results show that: The value of the selection probability n p of the QN operator also directly affects the solutionefficiency. Generally speaking, for strong nonlinear CNLE composed of multimodal functions, n pcanbelarger; For weakly nonlinear CNLE composed of functions with fewer extreme points and stronger monotonicity, n p can be smaller. It is demonstrated that the computational results show that this methodsignificantly outperforms the GA and QN methods.
