A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

Hongping Hu,Peng Wang,Linmei Zhang,Xiuhui Tan,Yanping Bai

doi:10.1109/access.2019.2932198

Hongping Hu, Peng Wang + Show 3 more

Open Access

https://doi.org/10.1109/access.2019.2932198

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Abstract

This paper proposes a hybrid algorithm called ISSA based on the combination of squirrel search algorithm (SSA) proposed in 2019 and invasive weed optimization (IWO) proposed in 2006. About 36 benchmark functions are employed to test the performances of ISSA. Then, ISSA is combined with support vector machine (SVM) and deterministic maximum-likelihood (DML) algorithm, respectively, and the two corresponding models ISSA-SVM and ISSA-DML are established for performing the grade classifications of air quality and the direction of arrival (DOA) estimation of MEMS vector hydrophone, respectively. The results of 36 benchmark functions prove that the proposed ISSA is able to provide very competitive results in terms of the average values, the standard derivation, and the convergence curves. The average accuracy rate of classification of ISSA-SVM model is the best and reaches 87.91971%, and the DOA estimations of ISSA-DML have the least root mean square error (RMSE) and the closest to the actual angles. Therefore, it is concluded that the proposed ISSA is an effective algorithm for function optimizations and is suitable to be combined with other algorithms and machine learning for classification and estimation.

Highlights

Many problems in the real world can be attributed to optimization problems
We find that the estimated direction of arrival (DOA) of ISSA-deterministic maximum-likelihood (DML) model is (20.14◦, 49.95◦) for two incident angles and (−10.16◦, 20.11◦, 50.55◦) for three incident angles, which are the closest to the known incident angles (20◦, 50◦) and (−10◦, 20◦, 50◦), respectively
The results shown that ISSA-DML is superior to ALO-DML, dragonfly algorithm (DA)-DML, Particle swarm optimization (PSO)-DML, invasive weed optimization (IWO)-DML and squirrel search algorithm (SSA)-DML models

Summary

INTRODUCTION

Many problems in the real world can be attributed to optimization problems. With the complexity of problems increasing, it is obvious that the need for optimization techniques becomes more and more. THE PROPOSED HYBRID ALGORITHM Every flying squirrel in SSA is only on one tree in the forest and every flying can have its offsprings in nature, which gives a inspire that the reproduction in IWO can be introduced into. The proposed improvement of ISSA is based on three cases of generating new locations in SSA, as follows: Case 1: FSat may move towards hickory nut tree. If R1 ≥ Pdp, determine the number of offspring of every flying squirrel on the hickory nut tree according to FIGURE 3, use the standard derivation obtained from. Statistical results obtained by ISSA,SSA,IWO,PSO,DA and ALO through 30 independent runs on functions F1(x) − F14(x) with 30 dimension. Statistical results obtained by ISSA,SSA,IWO,PSO,DA and ALO through 30 independent runs on functions F15(x) − F25(x) with fixed and lower dimension. Based on ISSA and SVM, the hybrid model is established, named by ISSA-SVM

EXPERIMENTAL RESULTS

THE HYBRID MODEL BASED ON DML AND ISSA

SIMULATION EXPERIMENTAL RESULTS

VIII. CONCLUSION