Abstract
This paper proposes a modified and improved grasshopper optimization algorithm (IGOA) for solving complex and challenging optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed bio-inspired swarm optimization algorithm which is based on the swarm nature of grasshoppers. It mainly relies upon the social interaction forces to find global optimum values of an optimization problem. However, it has a strong tendency to move towards the current optimal and hence may get trapped in local optimal points. This behaviour is strongly related to a factor, known as c factor, in GOA which varies linearly throughout the iterations from a maximum to minimum value. This work proposes two modifications in conventional GOA to avoid premature convergence of GOA. These modifications include a novel c factor variation scheme and inclusion of random walks between grasshoppers to attain global optimum points. The performance of IGOA is tested on 19 benchmark test functions for 20 independent trial runs. For all the cases, it was observed that performance of IGOA was superior to GOA and it outperformed the GOA in terms of accuracy, speed, and repeatability for all the considered test functions.
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