Merged Clustering Imperialists Algorithm (MCIA)

Abstract

This paper introduces an improved evolutionary algorithm based on the imperialist competitive algorithm (ICA) called merged clustering imperialists algorithm (MCIA). Merged clustering imperialist algorithm is another version of clustering imperialist algorithm (CIA). The imperialist competitive algorithm that has recently been introduced is used in order to optimize problems and has shown its reliable performance. This novel optimization algorithm is inspired by socio-political process of imperialistic competition in the real world. In the ICA, there are two categories of countries: Imperialists and Colonies that each Imperialist observes its colonies by absorption policy. In the proposed algorithm, the changed ICA is used for clustering data. In the MCIA, according to the number of considered clusters, the Imperialists will be created, and they act like Imperialists in the ICA and absorb their colonies. In this process which colonies move toward Imperialists, the Imperialists borders and regions are specified and by this way whole region is clustered into the number of Imperialists. When the region and border of clusters are specified, with a Merge operator that is different in various clustering problems, the clusters' components merge to each other. Finally, the proposed algorithm is used for clustering an image and a desired result is obtained.

Edge detection based on the Shannon Entropy by piecewise thresholding on remote sensing images

“…One imperialist with its colonies makes an empire. All the colonies of the initial population are divided among the mentioned imperialists based on their cost and function, respectively [17][18][19][20]. In this paper, the ICA algorithm was used to optimise data clustering.…”