Seismic Inversion Problem Using a Multioperator Whale Optimization Algorithm

Abstract

The whale optimization algorithm (WOA) is a metaheuristic algorithm based on swarm intelligence and it mimics the hunting behavior of whales. It has the imperfection of premature convergence into local optima. In order to overcome this disadvantage, a multioperator WOA (MOWOA) is proposed. Four main strategies are introduced to the MOWOA to heighten the search capacity of WOA. The strategies include nonlinear adaptive parameter design, an exploration mechanism of honey badger, Cauchy factor strategy, and greedy strategy. This paper tests the versatility of MOWOA with three different types of benchmark functions, and a kind of seismic inversion problem are trialed run. From the experimental results, the performance of MOWOA outperforms the compared algorithms in global optimization.

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

“…In addition, the quality of the inversion results is also related to the optimization method. A variety of optimization algorithms, such as artifcial bee colony (ABC) [34], PSO [35], gray wolf optimization (GWO) [36], GA [37], whale optimization algorithm (WOA) [38], and bat algorithm (BA) [39], applied in the model parameter identifcation have shown good adaptability. Compared with other methods, PSO has the characteristics of simple implementation, wide application, and strong global search capability.…”