A Rational Cooperative Foraging Based Grey Wolf Optimizer

In this paper, a rational cooperative foraging based  grey wolf optimizer (CFGWO) is proposed to enhance the performance of conventional grey wolf optimizer (GWO). It inherits the communicating behaviour of wolves during cooperative foraging in accordance with original leadership hierarchy based approach. The prime motivation is to direct every wolf through learning, communication, and cognitive processing. Here, wolves first  learn through original leadership hierarchy and then transfer the information via communication signalling. Also, acceleration coefficient is introduced to balance communication between wolves throughout iterations. In sum, these modifications direct the searching towards potential region in effective manner, avoid stagnation at suboptimal solution, and evade premature convergence. CFGWO is investigated on 31 synthesis benchmark unconstrained test functions of distinct characteristics; and results are compared with other six prominent algorithms for various analysis metrics and few results are presented. CFGWO is a viable and robust alternative to solve global and engineering optimization problems with balanced exploration and exploitation.