Article: Materialised views selection using ant lion optimiser Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2026 Vol.18 No.3/4 pp.390 - 410 Abstract: Materialised views significantly improve query performance by facilitating swift access to pre-computed data, resulting in faster response times due to their reduced size compared to complete data warehouses. The materialised view selection problem is a challenging aspect of data warehouse design that aims to choose an optimal subset of views within resource constraints. It is known to be an NP-complete problem. The ant lion optimiser (ALO) is inspired by antlions' hunting behaviour that has gained attention as a bio-inspired algorithm. Its ability to escape local optima stagnation makes it a promising tool for addressing computationally hard problems. This paper focuses on adapting the ALO algorithm for discrete optimisation tasks, specifically materialised view selection problem. The ALO algorithm undergoes strategic modification, incorporating tailored encoding schemes and modified operators inspired by genetic algorithms. The resulting genetic-ant lion optimiser (GALO) is proposed as a solution for the materialised view selection problem. Through experiments comparing GALO with recent metaheuristic algorithms like cuckoo search, artificial bee colony, and particle swarm optimisation, it is demonstrated that GALO excels in choosing higher-quality views for materialisation. This highlights the effectiveness of the customised ALO algorithm in tackling the discrete optimisation challenges posed by materialised view selection in the context of data warehouse design. Inderscience Publishers - linking academia, business and industry through research

Title: Materialised views selection using ant lion optimiser

Authors: Amit Kumar

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

Abstract: Materialised views significantly improve query performance by facilitating swift access to pre-computed data, resulting in faster response times due to their reduced size compared to complete data warehouses. The materialised view selection problem is a challenging aspect of data warehouse design that aims to choose an optimal subset of views within resource constraints. It is known to be an NP-complete problem. The ant lion optimiser (ALO) is inspired by antlions' hunting behaviour that has gained attention as a bio-inspired algorithm. Its ability to escape local optima stagnation makes it a promising tool for addressing computationally hard problems. This paper focuses on adapting the ALO algorithm for discrete optimisation tasks, specifically materialised view selection problem. The ALO algorithm undergoes strategic modification, incorporating tailored encoding schemes and modified operators inspired by genetic algorithms. The resulting genetic-ant lion optimiser (GALO) is proposed as a solution for the materialised view selection problem. Through experiments comparing GALO with recent metaheuristic algorithms like cuckoo search, artificial bee colony, and particle swarm optimisation, it is demonstrated that GALO excels in choosing higher-quality views for materialisation. This highlights the effectiveness of the customised ALO algorithm in tackling the discrete optimisation challenges posed by materialised view selection in the context of data warehouse design.

Keywords: materialised view selection problem; MVS; antlion optimiser algorithm; bio-inspired algorithm; combinatorial optimisation.

DOI: 10.1504/IJIIDS.2026.155344

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.3/4, pp.390 - 410

Accepted: 28 Dec 2025
Published online: 30 Jul 2026 *

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Title: Materialised views selection using ant lion optimiser

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