Modified grasshopper optimization algorithm-based genetic algorithm for global optimization problems: the system of nonlinear equations case study

Grasshopper optimization algorithm (GOA) is one of the promising algorithms for problems. However, it has main drawback trapping into a local minimum, which causes slow convergence or inability to detect solution. Several modifications and combinations were suggested overcome this problem. This paper presents modified grasshopper (MGOA)-based genetic Modifications rely on certain mathematical assumptions varying domain control parameter, Cmax, escape from minimum move search process an improved point. Parameter C essential parameters in GOA, where balances exploration exploitation space. These aim speed up rate by reducing repeated solutions number iterations. Both original GOA proposed are tested with 19 test functions investigate influence modifications. In addition, will be applied solve five different cases nonlinear systems types dimensions regularity show reliability efficiency algorithm. Promising results achieved compared GOA. The approach shows average percentage improvement 96.18 as illustrated detailed results.