Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling

Abstract

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 tremendous alternative method to conventional geophysical modeling techniques which suffer from dependence to initial model, linearization problems and being trapped at a local minimum. An area neglected in using PSO is joint modeling of geophysical data sets having different sensivities, whereas this kind of modeling with multiobjective optimization techniques has become an important issue to increase the uniqueness of the model parameters. However, using of subjective and unpredictable weighting to objective functions may cause a misleading solution in multiobjective optimization. Multiobjective PSO (MOPSO) with Pareto approach allows obtaining set of solutions including a joint optimal solution without weighting requirements. This chapter begins with an overview of PSO and Pareto-based MOPSO presented their mathematical formulation, algorithms and alternate approaches used in these methods. The chapter goes on to present a series synthetic modeled of seismological data that is one kind of geophysical data by using of Pareto-based multiobjective PSO. According to results matched perfectly, we believe that multiobjective PSO is an innovative approach to joint modeling of such data.