A multi-objective memetic inverse solver reinforced by local optimization methods

- We increase the accuracy of the local search in MO-HMS by means of gradient-based optimization methods.
- We use benchmarks to study the influence of parameters on our multi-objective selection operator.
- We solve the twin-objective magnetotelluric inverse problem using the enhanced MO-HMS.
- Using local methods delivers more good-quality Pareto solutions.

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.