Affiliations: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
Correspondence:
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Corresponding author. Heming Jia, College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China. E-mail: [email protected].
Abstract: Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper’s behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces.
