Ant Colony Optimization Algorithms: Introductory Steps to Understanding

Oliazadeh, Arman; Bozorg‐Haddad, Omid; Arefinia, Ali; Ahmad, Sajjad

doi:10.1007/978-981-19-2519-1_7

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…, θ s } . In a natural setting, ants use information about pheromone concentration to find the shortest path between their nest and food source [38]. This ant behavior serves as the inspiration for the bio-inspired ACO algorithm.…”

Section: The Aco Algorithmmentioning

confidence: 99%

Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks

Alam,

Chugh,

Azad

et al. 2024

J Wireless Com Network

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The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users’ throughput needs while reducing network imbalances and call drop rates.

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Section: The Aco Algorithmmentioning

confidence: 99%