LAUSR

Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.