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Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.
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This content will become publicly available on May 7, 2026
Optimizing Personalized Learning Pathways with the Salp Swarm Algorithm: A Novel Approach
The challenge of optimizing personalized learning pathways to maximize student engagement and minimize task completion time while adhering to prerequisite constraints remains a significant issue in educational technology. This paper applies the Salp Swarm Algorithm (SSA) as a new solution to this problem. Our approach compares SSA against traditional optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results demonstrate that SSA significantly outperforms these methods, achieving a lower average fitness value of 307.0 compared to 320.0 for GA and 315.0 for PSO. Furthermore, SSA exhibits greater consistency, with a lower standard deviation and superior computational efficiency, as evidenced by faster execution times. The success of SSA is attributed to its balanced approach to exploration and exploitation within the search space. These findings highlight the potential of SSA as an effective tool for optimizing personalized learning experiences
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- Award ID(s):
- 2142360
- PAR ID:
- 10595233
- Publisher / Repository:
- IEEE 2025 6th International Conference on Artificial Intelligence, Robotics, and Control
- Date Published:
- Format(s):
- Medium: X
- Location:
- Savannah, GA
- Sponsoring Org:
- National Science Foundation
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