Development of a new multi-objective local search considering local Pareto optimality and its application to inverse problem

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A new multi-objective local search approach considering local Pareto optimality has been proposed. This approach also has an interpolation mechanism for capturing the whole of Pareto subsets. Through the numerical examples, the effectiveness of the proposed approach could be indicated.Furthermore, a new approach based on Evolutionary Multi-criterion Optimization (EMO) for sparse CT problem were developed. This approach incorporates Gerchberg -Saxton algorithm (GS algorithm) that is the fact standard method in the field of phase retrieval problem as optimization tool and implements an original genetic operators utilizing the characteristics of strength distribution. The superiority of our approach could be confirmed by comparison to the existing approaches in sparse CT problem.