Genetic algorithms combined with nonlinear programming for multimodal function optimization

The genetic algorithm has strong global searching ability,but its local searching ability is weak.Generally,it could only reach the second-best solution of the function optimization problem,not the optimal one.When the function has multiple peaks,the genetic algorithm is easier to fall into the local minimum,and can not find the global minimum.With the gradient descent method,nonlinear programming has strong local searching ability for the function optimization problem.Therefore,this paper takes the advantage of genetic algorithm and nonlinear programming for multimodal optimization.On one hand,it uses genetic algorithm for global optimization,and on the other hand,it employs nonlinear programming for local optimization.The experimental results show that the method can not only solve the problem that the multi-modal function optimization would easily fall into the local minimum,but also have high iterative optimization efficiency,and could obtain satisfactory results.