A SOMA-inspired Hybrid Variant of Grasshopper Optimization Algorithm for Global Optimization

Abstract

The Grasshopper Optimization Algorithm (GOA) is a relatively recent population-based stochastic search algorithm extensively used for solving various nonlinear global optimization problems arising in science and engineering. Like other evolutionary algorithms, this algorithm also has some limitations like poor balance between exploration and exploitation, requires large population size, and premature convergence. To address these limitations and to improve the efficiency of GOA, two hybridized variants of GOA have been proposed in this paper. In these variants, GOA is combined with the feature of another population-based algorithm which is the Self-Organizing Migrating Algorithm (SOMA). First GOA is combined with the exploitation feature of SOMA and a hybrid variant of SOMGOA is proposed. Later to balance exploitation, SOMGOA is merged with tournament selection to maintain the good quality solution of previous and current generations and SOMGOA-t is presented. The effectiveness of both the variants is analysed based on results and comparative analysis is made against the results of GOA and SOMA. A total of twenty-one standard benchmark functions with different intrinsic difficulties and four unconstrained optimization problems (gear train design, frequency modulation sound parameter identification problem, Gas transmission compressor design problem, and Optimal capacity of gas production facility) have been used for testing. The analysis of experimental results involved two statistical tests: the Wilcoxon rank-sum test and the Friedman statistical test. Furthermore, the statistical findings consistently affirm the superiority of the SOMGOA-t when compared to the alternative algorithms (GOA and SOMA). However, the present study is limited to solving unconstrained nonlinear optimization problems.