Abstract
Search is considered to be an important functionality in a computational system. Search techniques are applied in file retrievals and indexing. Though there exists various search techniques, binary search is widely used in many applications due to its advantage over other search techniques namely linear and hash search. Binary search is easy to implement and is used to search for an element in a large search space. The worst case time complexity of binary search is O (log 2 n) where n is the number of elements (search space) in the array. However, in binary search, searching is performed on the entire search space. The complexity of binary search may be further reduced if the search space is reduced. This paper proposes an Ant Colony Optimization based Binary Search (ACOBS) algorithm to find an optimal search space for binary search. ACOBS algorithm categorizes the search space and the key element is searched only in a specific category where the key element can exist thereby reducing the search space. The time complexity of ACOBS algorithm is O (log 2 c) where c is the number of elements in the reduced search space and c < n. The proposal is best suited for real time applications where searching is performed on a large domain.
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Sreelaja, N.K., Sreeja, N.K. (2021). An Ant Colony Optimization Based Approach for Binary Search. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_28
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DOI: https://doi.org/10.1007/978-3-030-78743-1_28
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