Improved social spider optimization algorithm

Abstract

The invention discloses an improved social spider optimization algorithm, and the algorithm comprises the specific steps: S1, initializing a spider population through employing a chaotic mapping reverse learning strategy, and setting each parameter in the population; S2, calculating a nonlinear dynamic probability factor PF; S3, calculating an individual fitness value, a weight value and a vibration sensing force of the spider; S4, calculating a nonlinear inertia weight, and updating the position of the female spider through the result of the nonlinear inertia weight; S5, performing male and female spider mating operation according to the mating radius, and replacing the worst spider position in the population by the generated filial generation through greedy selection; S6, performing disturbance on the positions of the spider individuals by Gaussian variation; S7, judging whether the maximum number of iterations is reached or not, if the maximum number of iterations is reached, entering the step S8, and otherwise, entering the step S3; and S8, ending the cycle operation, and outputting a result. The SSO algorithm is improved, so that the global search capability of the algorithm is improved, and the convergence speed and the convergence precision are improved.

Description

technical field [0001] The invention relates to the field of computer technology and the field of swarm intelligence optimization algorithms, in particular to an improved swarm spider optimization algorithm. Background technique [0002] With the development of science and technology, optimization problems exist in all aspects of daily life, and the requirements for solutions to optimization problems are also increasing. Aiming at the limitations of traditional algorithms when solving complex problems, the proposed swarm intelligence optimization algorithm provides more accurate approximate solutions for solving complex problems. At present, swarm intelligence optimization algorithms have been widely used in fields such as mechanical design, robotics, signal processing, economics, modern agriculture, and operations research. However, there are still some problems in the swarm intelligence algorithm, such as complicated parameter setting, the algorithm has fallen into a loca...

AI Technical Summary

Problems solved by technology

But like other intelligent algorithms, as the number of iterations increases, the population diversity of male and female spiders in the SSO algorithm decreases, the algorithm tends to fall into local optimum, and the global search ability decreases, which eventually makes the algorithm converge slowly and the convergence accuracy is low.

In recent years, some scholars have conducted research on the shortcomings of the SSO algorithm. For example, Qiu J et al. proposed the wDESSO algorithm (Qiu, J., Xie, J., Cheng, F., Zhang, X., & Zhang, L. ..(2017).A hybrid social spider optimization algorithm with differential evolution for global optimization.Journal of Universal Computer Science, 23(7),619-635.), to a certain extent, improved the balance between global search ability and local search ability, while The convergence speed and convergence precision have also improved to a certain extent, but the ability of the algorithm is still insufficient, and there are still problems of insufficient balance between global search capability and local search capability, slow convergence speed and low convergence precision.

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