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A Gravitational Search Algorithm with Adaptive Mixed Mutation for Function Optimization

Volume 14, Number 4, April 2018, pp. 681-690
DOI: 10.23940/ijpe.18.04.p11.681690

Jingsen Liua,b, Yuhao Xingb, and Yu Lic

aInstitute of Intelligent Network system, Henan University, Kaifeng, 475004, China
bCollege of Software, Henan University, Kaifeng, 475004, China
cInstitute of Management Science and Engineering, Henan University, Kaifeng, 475004, China

(Submitted on January 10, 2018; Revised on February 24, 2018; Accepted on March 27, 2018)

Abstract:

In order to improve the optimization accuracy and convergence speed of gravitational search algorithm, the gravitational search algorithm with the mechanism of adaptive mixed random mutation is proposed. A mutation trigger function with adaptive property is introduced into the algorithm, so that every particle has the probability of mutation at any time, and the number of particles that change in the population tends to decrease with the increase of iteration times. At the same time, in the whole optimization process of the algorithm, the uniform mutation and Laplace-normal hybrid mutation cooperate together. The uniform mutation enables the algorithm to find the global optimal area quickly, and then continue deep search with hybrid mutation to improve the optimization performance. The simulation results show that in solving the problem of extremum optimization, the improved algorithm has significantly improved optimization performance, and has high convergence accuracy and faster convergence speed.

 

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