Faculty of Computer Science and Information Technology
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Item LBP-CA: A Short-term Scheduler with Criticality Arithmetic(University of Hertfordshire, 2022) Fadlelseed, Sajid; Kirner, Raimund; Menon, CatherineIn safety-critical systems a smooth degradation of services is a way to deal with resource shortages. Criticality arithmetic is a technique to implement services of higher criticality by several tasks of lower criticality. In this paper, we present LBP-CA, a mixed-criticality scheduling protocol with smooth degradation based on criticality arithmetic. In the experiments we show that LPB-CA can schedule more tasks than related scheduling protocols (BP and LBP) in case of resource shortage, minimising the negative effect on low-criticality services. This is achieved by considering information about criticality arithmetic of services.Item Artificial Ecosystem‑Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems(International Journal of Computational Intelligence Systems, 2023) Al‑Shourbaji, Ibrahim; Kachare, Pramod; Fadlelseed, Sajid; Jabbari, Abdoh; Hussien, Abdelazim G.; Al‑Saqqar, Faisal; Abualigah, Laith; Alameen, AbdallaMeta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Opti mization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competi tive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.