Abstract:Gray wolf optimizer (GWO) is a new heuristic algorithm. It has few parameters and strong optimization ability and is used in many fields. However, when solving complex and multimodal functions, it is also easy to trap into the local optimum and premature convergence. In order to boost the performance of GWO, a tent chaotic map and opposition-based learning Grey Wolf Optimizer (CO-GWO) is proposed. Firstly, some better values of the population in the current generation are retained to avoid deterioration in the… Show more
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