in: Optimization Algorithms, Nodari Vakhania, Editor, Open Text Corporation , London, pp.84-104, 2021
It has been recently revealed that particle swarm optimization (PSO) is a modern global optimization method and it has been used in many real-world engineering problems to estimate model parameters. PSO has also led as a tremendous alternative method to conventional geophysical modeling techniques that suffer from initial model dependence, linearization problems, and being trapped at a local minimum. One area that is neglected in the use of PSO is the joint modeling of geophysical datasets that have different sensitivities, whereas this kind of modeling with multiobjective optimization techniques has become an important issue to increase the uniqueness of the model parameters. However, the use of subjective and unpredictable weighting to objective functions can lead to a misleading solution in multiobjective optimization. Multiobjective PSO (MOPSO) with the Pareto approach allows to obtain a set of solutions that contains a joint optimal solution without weighting requirements. This chapter begins with an overview of the PSO and Pareto-based MOPSO and presents their mathematical formulation, algorithms, and alternative approaches used in these methods. The chapter goes on to present a series of synthetic modeling of seismological data, which is a type of geophysical data by using Pareto-based multiobjective PSO. According to results matched perfectly, we believe that multiobjective PSO is an innovative approach to joint modeling of such data.