Enhanced Chaotic Grey Wolf Optimizer for Real-World Optimization Problems

Abstract

The gray wolf optimizer (GWO) is a new population-based optimizer that is inspired by the hunting procedure and leadership hierarchy in gray wolves. In this chapter, a new enhanced gray wolf optimizer (EGWO) is proposed for tackling several real-world optimization problems. In the EGWO algorithm, a new chaotic operation is embedded in GWO which helps search agents to chaotically move toward a randomly selected wolf. By this operator, the EGWO algorithm is capable of switching between chaotic and random exploration. In order to substantiate the efficiency of EGWO, 22 test cases from IEEE CEC 2011 on real-world problems are chosen. The performance of EGWO is compared with six standard optimizers. A statistical test, known as Wilcoxon rank-sum, is also conducted to prove the significance of the explored results. Moreover, the obtained results compared with those of six advanced algorithms from CEC 2011. The evaluations reveal that the proposed EGWO can obtain superior results compared to the well-known algorithms and its results are better than some advanced variants of optimizers.