Group-based synchronous-asynchronous Grey Wolf Optimizer

Grey Wolf Optimizer represents a relatively new metaheuristic scheme for solving continuous optimization problems. In spite of its interesting characteristics, it presents several flaws such as lack of accuracy, low diversity, premature convergence and imbalance between exploitation and exploration. In this paper, a modified version of the Grey Wolf Optimizer called Group-based Synchronous-Asynchronous Grey Wolf Optimizer is introduced. The proposed scheme incorporates a synchronous-asynchronous processing scheme, a set of different nonlinear functions and an operation to increase diversity. With such mechanisms, the proposed algorithm presents a better balance between exploration and exploitation, an increment in the accuracy and the ability to avoid the convergence in local minima. Such capacities allow its use in complex engineering problems that involve highly multimodal objective functions with a difficult localization of their global optimum. To evaluate the performance of the proposed approach, it has been tested on a representative number of functions of the well-known IEEE Congress on Evolutionary Computation 2017 benchmark set of functions and real-world engineering problems. The results of our method have been compared against those produced by other states of the art metaheuristic algorithms. The results prove the effectiveness and accuracy of the proposed technique. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a modified version of Grey Wolf Optimizer, which achieves a better balance, increased accuracy, and avoids convergence at local minima through synchronous-asynchronous processing and increasing diversity operations.