Year-end Sale % OFF 
Abstract
The Rosenbrock function optimization belongs to unconstrained optimization problems, and its global minimum value is located at the bottom of a smooth and narrow valley of the parabolic shape. It is very difficult to find the global minimum value of the function because of the little information provided for the optimization algorithm. According to the characteristics of the Rosenbrock function, this paper specifically proposed an improved differential evolution algorithm that adopts the self-adaptive scaling factor F and crossover rate CR with elimination mechanism, which can effectively avoid premature convergence of the algorithm and local optimum. This algorithm can also expand the search range at an early stage to find the global minimum of the Rosenbrock function. Many experimental results show that the algorithm has good performance of function optimization and provides a new idea for optimization problems similar to the Rosenbrock function for some problems of special fields.
Highlights
Differential evolution (DE) is a simple yet powerful search technique introduced by Storn and Price in 1995 [1]
The accuracy of the experimental test is set to 1e − 4, which means that if the result reaches a difference of 1e − 4 from the target value and tends to be constant, we can conclude that the iteration is over
We perform each algorithm of the other literature20 times and record the number of times it takes to reach the number of stable iterations to verify the superiority of the newly improved differential evolution algorithm
Summary
Differential evolution (DE) is a simple yet powerful search technique introduced by Storn and Price in 1995 [1]. DE has special memory ability to track current search status dynamically to adjust the strategy of search This capability indicates that the DE algorithm has strong global convergence ability and robustness without relying on the feature information of the problem, and the DE algorithm is suitable for solving optimization problems that cannot be solved by conventional mathematical programming methods in complex environments. This paper proposes an improved differential evolution algorithm with selfadaptive mutation and control parameters to solve this kind of problem and optimize the characteristic design of the Rosenbrock function In this algorithm, the scaling factor F can be adaptively changed by the previous learning experience, and the level of the crossover rate CR depends on the fitness value of the individual. Experiments show that the improved DE algorithm has better convergence speed and accuracy than other types of improved schemes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
