A multi objective memetic inverse solver reinforced by local optimization methods

We propose a new memetic strategy that can solve the multi-physics, complex inverse problems, formulated as the multi-objective optimization ones, in which objectives are misfits between the measured and simulated states of various governing processes. The multi-deme structure of the strategy allows for both, intensive, relatively cheap exploration with a moderate accuracy and more accurate search many regions of Pareto set in parallel. The special type of selection operator prefers the coherent alternative solutions, eliminating artifacts appearing in the particular processes. The additional accuracy increment is obtained by the parallel convex searches applied to the local scalarizations of the misfit vector. The strategy is dedicated for solving ill-conditioned problems, for which inverting the single physical process can lead to the ambiguous results. The skill of the selection in artifact elimination is shown on the benchmark problem, while the whole strategy was applied for identification of oil deposits, where the misfits are related to various frequencies of the magnetic and electric waves of the magnetotelluric measurements. 2016 Elsevier B.V

hp-HGS strategy for inverse AC/DC resistivity logging measurement simulations

In this paper we present resistivity logging measurement simulation with the usage of two types of borehole logging devices: operating with zero frequency (direct current, DC) and those operating with higher frequencies (alternate current, AC). We perform simulations of 3D resistivity measurements in deviated wells, with the sharp angle between the borehole and formation layers. We introduce hierarchical adaptive genetic strategy $hp-HGS$ interfaced with adaptive finite element method. We apply the strategy for the solution of the inverse problem, where we identify the resistivities of the formation layers based on a given measurement. We test the strategy on both direct and accurate currect cases.

Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization

In this paper we introduce the Multiobjective Optimization Hierarchic Genetic Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary optimization in a hierarchy of populations. The maturing mechanism improves growth and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark problems and their modifications for which single fitness evaluation time depends on the solution accuracy. We compare the proposed algorithm with the Island Model Genetic Algorithm as well as with single-deme methods, and discuss the impact of internal algorithms on the MO-mHGS meta-algorithm. © 2016 Elsevier B.V