Abstract
To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science.
Highlights
The differential evolution (DE) algorithm was proposed by Storn and Price in 1995 [1,2]
Through the theoretical analysis of algorithm factors and performance testing of standard test functions, the results show that when the adaptive dynamic disturbance strategy for differential evolution (ADDSDE) algorithm is used to solve complex optimization problems such as high-dimensions and multi-peaks, the global optimal solution can be obtained with a minimum number of iterations, and the algorithm has strong robustness
The adaptive dynamic disturbance strategy for differential evolution (ADDSDE) algorithm mainly improves the algorithm from four aspects of population initialization, parameter adaptation, mutation strategy and disturbance strategy, and comprehensively improves the global optimization search ability and convergence speed of the algorithm
Summary
The differential evolution (DE) algorithm was proposed by Storn and Price in 1995 [1,2]. The binary variables evolved based on the genetic algorithm and the continuous variables evolved based on the DE algorithm, which was used to solve a nonlinear, high-dimensional, highconstrained and mixed-integeroptimization problem These classical improved algorithms improve the optimization performance of the DE algorithm to a certain extent, but for some high-dimensional and complex problems, there are still disadvantages of falling into the local optimum. Through the theoretical analysis of algorithm factors and performance testing of standard test functions, the results show that when the adaptive dynamic disturbance strategy for differential evolution (ADDSDE) algorithm is used to solve complex optimization problems such as high-dimensions and multi-peaks, the global optimal solution can be obtained with a minimum number of iterations, and the algorithm has strong robustness
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