A Whale Optimization Algorithm Based on Oscillatory Convergence and Diversity Variation for Complex Defect Profile Inversion in Oil and Gas Pipelines

Magnetic leakage detection is one of the most commonly used methods for pipeline inspection, which mainly uses magnetic sensors to detect the magnetic leakage field on the internal and external surfaces of the pipeline to determine whether there are defects in the pipeline. The defect quantification algorithm includes a forward model and an optimization algorithm, in which the estimation of target defects using optimization algorithms is one of the key aspects of defect inversion. Most of the existing optimization algorithms are based on particle swarm algorithms (PSOs) and genetic algorithms (GAs), which are prone to premature problems and have low convergence accuracy. To address the problems in the process of defect inversion, this paper proposes a new inversion algorithm, which obtains part of the prior knowledge from the application context of defect inversion, and adopts the decay oscillation function as the nonlinear convergence factor based on the whale optimization algorithm (WOA). In addition, referring to the concepts of “genetic” and “mutation” in the GA, a diversity variation strategy based on dynamic step size is designed. The algorithm designed has the advantages of fast operation and high search accuracy. At the end of the paper, two sets of experiments are designed to compare the improved WOA with other existing optimization algorithms. The results demonstrate that the algorithm is significantly superior to other algorithms, both in the ideal case of simulation experiments and in the practical application of defect inversion.