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Ant Colony Optimization Approach To Digital Comparative Holography Through Traveling Salesman Problem

Digital comparative holography is an essential mechanism used for working on verifying the body or contortion of two corresponding entities with varying micro architecture. Ant Colony Optimization (ACO) is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. The Traveling Salesman Problem (TSP), given a list of nodes and the distances between each node pairs, describes the shortest possible route that visits each node exactly once and returns to the originating node. The TSP has been successfully deployed with ACO to explain and justify many existing optimization issues. Here in this research work, it has been demonstrated how the joint ACO-TSP notion can be used for optimization purposes in digital comparative holography’s context.