Optimal Foraging Algorithm Based on Differential Evolution

Yanming Fu,Weizhi Zhang,Baohua Huang,Chiwen Qu

doi:10.1109/access.2020.2966856

Yanming Fu, Weizhi Zhang + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.2966856

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Abstract

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.

Highlights

Optimization is the process of finding the best solutions for a problem in a specific situation
By introducing mutation operator and crossover operator of differential evolution mechanism, the differential evolution optimal foraging algorithm (DEOFA) strengthens the information exchange among individuals, overcomes the optimal foraging algorithm (OFA) defects of weak global search ability and slow convergence rate in the later iteration stage. 30 benchmark functions are tested on nine contrastive swarm intelligence algorithms, and the test results are compared and analyzed in convergence curves, variance charts, Wilcoxon rank sum test
Experiment shows that the DEOFA increases the OFA optimization accuracy, and improves the OFA convergence speed and stability

Summary

INTRODUCTION

Optimization is the process of finding the best solutions for a problem in a specific situation. The precise algorithm takes enormous computing time, so it is not suitable to solve high-dimensional or complex optimization problems. In the absence of local information and mathematical models, swarm intelligence algorithm can still find the optimum solutions of complex problems. It is suitable for solving complex or high-dimensional optimization problems. Zhu and Zhang [18] proposed an optimal foraging algorithm (OFA) based on the animal optimal foraging theory in 2017. The OFA has better optimization ability and convergence speed compared with the current swarm intelligence algorithms such as particle swarm optimization algorithm, differential evolution algorithm, etc [18]. Compared with OFA, the DEOFA has better convergence speed, solution accuracy and stability

OPTIMAL FORAGING ALGORITHM

INTRODUCING DIFFERENTIAL EVOLUTION MECHANISM TO IMPROVE OFA

EXPERIMENTAL SIMULATION

CONCLUSION