Differential Evolution Algorithm Based on Simulated Annealing

Abstract

Differential evolution algorithm is a simple stochastic global optimization algorithm.In this paper, the idea of simulated annealing is involved into original differential evolution algorithm and a simulated annealing-based differential evolution algorithm is proposed. It is almost as simple for implement as differential evolution algorithm, but it can improve the abilities of seeking the global excellent result and evolution speed. The experiment results demonstrate that the proposed algorithm is superior to original differential evolution algorithm.

Influence Mechanism of External Social Capital of University Teachers on Evolution of Generative Digital Learning Resources of Educational Technology of University Teachers - Empirical Analysis of Differential Evolution Algorithm and Structural Equation Model of Bootstrap Self-extraction Technique

“…Excellent samples that will move into the next generation of population groups are used as the empirical analysis samples of structural equation model (SEM) based on Bootstrap selfextraction technique. The main purpose of differential evolution algorithm is to search for individual samples with good fault-tolerant and strong learning ability, so that better individuals with strong learning ability will enter into the next generation groups to maximize the overall search function (Li, Guo, Li, & Liu, 2016;Guo, Li, & Li, 2014;Deb, 2000). Based on the collected samples of 500 university professional teachers, this paper uses differential evolution algorithm, and uses software Matlab and software Stata to set executive parameters, as shown in Table 3.…”

“…The related principles, modeling steps and processes of differential evolution algorithm are as follows (Li, Guo, Li, & Liu, 2016;Guo, Li, & Li, 2014;Deb, 2000). Differential evolution algorithm includes four basic operations: population initialization, variation, crossover and selection, and the modeling steps and processes are population initialization, variation based on difference, crossover, selection, termination, output, feedback (Li, Guo, Li, & Liu, 2016;Guo, Li, & Li, 2014;Deb, 2000).…”

“…Differential evolution algorithm includes four basic operations: population initialization, variation, crossover and selection, and the modeling steps and processes are population initialization, variation based on difference, crossover, selection, termination, output, feedback (Li, Guo, Li, & Liu, 2016;Guo, Li, & Li, 2014;Deb, 2000). The main procedures and calculation sections are as follows: Among them, D is the dimension contained by the individual.…”

Metaheuristic Techniques in Enhancing the Efficiency and Performance of Thermo-Electric Cooling Devices

“…DE employ only three parameters as input to control the searching process, which is less than SA and GA. DE is currently among the most popular metaheuristics for solving single-objective optimization problems within continuous search spaces [20]. DE is generally a simple, and powerful algorithm that can outperform GA in many numerical single-objective optimization problems [40]. DE is able to explore the decision space more efficiently than GA even when multiple objectives need to be optimized [41].…”

“…Finally, the fitness of the resulting solutions is evaluated in the selection process and the target vector of the population competes against a trial vector to determine which one will be retained in the next generation, as shown in Equation (40). The operation procedure of DE is similar to that of GA but has a small difference with GA in the mutation period.…”