Abstract
Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.
Highlights
Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems
A novel periodic learning ontology matching model based on interactive GOA proposed in30 considered the periodic feedback from users during the optimization process, using a roulette wheel method to select the most problematic candidate mappings to present to users, and take a reward and punishment mechanism into account for candidate mappings to propagate the feedback of user, which is conducted on two interactive tracks from Ontology Alignment Evaluation Initiative. In31, a dynamic population quantum binary grasshopper optimization algorithm based on mutual information and rough set theory for feature selection is performed in twenty UCI datasets
For the unimodal function F6(x), the average value of the optimal value obtained by IGAO is 2.0048E+00, which is less than that obtained by GOA and is more than that obtained by particle swarm optimization (PSO)
Summary
Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. The improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. Swarm intelligence algorithms recently proposed have been applied to solve the optimization models obtained from different actual problems. In “Applications”, IGOA is utilized to optimize the parameters of BP neural network (BPNN) for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. Based on the influence of the gravity force not to be considered in the basic GOA, the right side of the Eq (7) minus the sum of the product between the gravitational constant g and the unit vector from the ith grasshopper to the jth grasshopper, the new updated position of the grasshopper is obtained as follows. According to the selected probability p , the position of the ith grasshopper is updated as follows
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