Abstract
Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.
Highlights
The convergence of the basic Differential Evolution is closely related to its control parameters
A hybrid algorithm based on frog leaping algorithm and Differential Evolution is suggested in this article, which combines the advantages of Shuffled Frog Leaping Algorithm (SFLA) and DE, in order to obtain a significant performance on convergence speed and the accuracy
It can be seen from the above experimental results that Shuffled Frog-Leaping Differential Evolutionary (SFDE) does better in most of the base functions compared with other differential evolutionary algorithms, and the convergence speed is accelerated
Summary
The convergence of the basic Differential Evolution (abbreviation for DE) is closely related to its control parameters. In [1], a method of Self-adaptive Differential Evolution algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each vector has its own mutation, cross parameters, and control strategies. Reference [13] proposed a Self-adaptive Differential Evolution algorithm with Improved Mutation Mode (IMMSADE), which improves the mutation model of DE and introduces some new control parameters. A hybrid algorithm based on frog leaping algorithm and Differential Evolution is suggested in this article, which combines the advantages of SFLA and DE, in order to obtain a significant performance on convergence speed and the accuracy.
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