An Improved Grasshopper Optimizer for Global Tasks

Abstract

The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.

Novel Variants of Grasshopper Optimization Algorithm to Solve Numerical Problems and Demand Side Management in Smart Grids

“…To further evaluate the performance of the proposed NVGOA variants, in this section, the NVGOA2_3, NVGOA1_3, and NVGOA4 variants' performance has been compared with previous enhancements of GOA on CEC-2017 benchmark functions. Those variants include SCFGOA (Saxena, 2019), OLCGOA (Z. , AGOA (Wang et al, 2021), IGOA (Luo et al, 2018), and MOLGOA (Zhou et al, 2020).…”

Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids

“…Zhao et al (Zhao et al 2019) introduced another enhancement based on integrating random jumping and dynamic weight strategy. Zhou et al (Zhou et al 2020) introduced three strategies; Cauchy mutation, genetic mutation, and orthogonal learning; to integrate into GOA and tested them on CEC2017 functions. Ewees et al (Ewees et al 2018) introduced an enhanced version of GOA by incorporating opposition-based learning with GOA to tackle 23 benchmark functions and some design problems in the engineering field such as the design of welded beams, pressure vessels, tension/compression springs, and three-bar truss.…”

“…Those variants include SCFGOA (Saxena 2019), OLCGOA (Z. Xu et al 2020a, b), AGOA (Wang et al 2021), IGOA (Luo et al 2018), and MOLGOA (Zhou et al 2020).…”