A memory-based Grey Wolf Optimizer for global optimization tasks

Abstract

Grey Wolf Optimizer (GWO) is a new nature-inspired metaheuristic algorithm based on the leadership and social behaviour of grey wolves in nature. It has shown potential to solve several real-life applications, but still for some complex optimization tasks, it may face the problem of getting trapped at local optima and premature convergence. Therefore, in this study, to prevent from these drawbacks and to get a more stable sense of balance between exploitation and exploration, a new modified GWO called memory-based Grey Wolf Optimizer (mGWO) is proposed. In the mGWO, the search mechanism of the wolves is modified based on the personal best history of each individual wolves, crossover and greedy selection. These strategies help to enhance the global exploration, local exploitation and an appropriate balance between them during the search procedure. To investigate the effectiveness of the proposed mGWO, it has been tested on standard and complex benchmarks given in IEEE CEC 2014 and IEEE CEC 2017. Furthermore, some real engineering design problems and multilevel thresholding problem are also solved using the mGWO. The results analysis and its comparison with other algorithms demonstrate the better search-efficiency, solution accuracy and convergence rate of the proposed mGWO in performing the global optimization tasks.