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Identification of objects referring to the same entity in different datasets is known as objects matching, which is both directly and indirectly used in a wide range of applications including conflation, quality assessment, data updating, and multi-scale analysis. Hence, a novel object matching approach is presented in this article, in which, in addition to take only geometric property into account, i.e. geometric and topological criteria, extracted from objects, any initial dependency on empirical parameters such as threshold of spatial similarity degree, buffer distance, and metric weights is eliminated, through which matching procedure may then be conducted in different datasets. All the relations in the proposed approach are considered including: one-tonull, null-to-one, one-to-one, one-to-many, many-to-one, and many-to-many. Moreover, efficiency of linear object matching using Real Coded Genetic Algorithm (RCGA), Particle Swarm Optimization (PSO) algorithm, and Artificial Bee Colony (ABC) algorithm in different datasets were investigated through optimization of geometric criteria. In order to assess the efficiency of the proposed approach, three datasets of different scales from various sources were used. As indicated by the results, the proposed framework was able to appropriately identify corresponding objects in different datasets. Additionally, it was revealed that GA outperformed the other two algorithms in terms of optimizing the parameters present in linear object matching.

Assessment of Optimization Algorithms on Multi-scale Matching of Spatial Datasets Based on Geometric Properties