Title: Swap operator-based grey wolf optimiser for materialised view selection problem

Authors: Amit Kumar

Addresses: Department of Information Technology, Rajkiya Engineering College Ambedkar Nagar, Uttar Pradesh, 224122, India

Abstract: Grey wolf optimiser (GWO) is a novel biological intelligence algorithm that primarily simulates the natural leadership structure and hunting behaviour of grey wolves. It has the ability to decrease the operating time for higher dimensional data by partitioning the large, complicated problems into smaller sub problems and distributing the subsets of operations to each agent. Therefore, it is widely employed in a variety of time-consuming tasks, including NP-hard problems. Materialised view selection (MVS) is an NP-hard discrete optimisation problem in the design process of a data warehouse that significantly speeds up query processing. Therefore, in this paper, GWO has been discretised using swap operators and swap sequence operators to address the MVS problem. Experimentally, it is observed that the proposed swap operator-based grey wolf optimiser (SOGWO) algorithm selects better quality views for materialisation than those selected using well-known metaheuristic algorithms over a number of view selection problem instances.

Keywords: grey wolf optimiser; GWO; data warehouse; materialised view; view selection; swap operator.

DOI: 10.1504/IJBIC.2024.140111

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.1, pp.1 - 11

Received: 12 Sep 2022
Accepted: 14 Nov 2023
Published online: 24 Jul 2024 *

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