An Improved Grasshopper Optimization Algorithm for Global Optimization

We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper (GOA) is a recently proposed meta-heuristic inspired by the swarming behavior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we based on logistic Chaos maps opposition-based learning strategy cloud model inertia weight (CCGOA). CCGOA divided three stages. chaos opposition initialization used to initialize population, that population can be evenly distributed in feasible solution space much possible, improve uniformity diversity initial distribution algorithm. introduced algorithm, different strategies are adjust speed Based principle chaotic maps, depth search carried out reduce probability optimum. Fourteen benchmark functions engineering example simulation verification. Experimental results show superior performance determining optimal test function problem.