A Modified Whale Optimization Algorithm and Its Application in Seismic Inversion Problem

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm which simulates the hunting behavior of humpback whales. WOA has the deficiency of easily falling into the local optimal solutions. In order to overcome the weakness of the WOA, a modified variant of WOA called OCDWOA is proposed. There are four main operators introduced into the OCDWOA to enhance the search performance of WOA. The operators include opposition-based learning method, nonlinear parameter design, density peak clustering strategy, and differential evolution. The proposed algorithm is tested on 19 optimization benchmark functions and a seismic inversion problem. OCDWOA is compared with the classical WOA and three typical variants of WOA. The results demonstrate that OCDWOA outperforms the compared algorithms in terms of obtaining the global optimal solution.

Automated summary

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm that can easily fall into local optimal solutions. To overcome this issue, a modified variant called OCDWOA is proposed, which introduces four operators to enhance the search performance of WOA. Experimental results show that OCDWOA outperforms other algorithms in obtaining the global optimal solution.