Fruit fly optimization algorithm (FOA) is a well-established meta-heuristic method with a clear core concept, a straightforward computation, and a code framework that is easy to build. In the case of large scale and multifaceted practical problems, the optimization effect of FOA may be unsatisfactory, and it is prone to stagnation. In this paper, to enrich the exploration and exploitation capability of the classic FOA, an effective whale-inspired hunting strategy is introduced to replace the random search plan of the original FOA, which we named it as WFOA. The proposed WFOA is compared with 9 state-of-the-art FOA's variants on a comprehensive set of 23 benchmark set and 30 IEEE CEC 2014 functions and advanced algorithms on a set of 21 test functions to validate its effectiveness. In addition, the effectiveness of WFOA is also verified on 20 IEEE CEC 2011 benchmark problems for tackling real-world problems. The statistical data shows that developed components effectively expand the exploration and exploitation capacity of the original FOA. (C) 2020 Elsevier Ltd. All rights reserved.