TRIBES Optimization Algorithm Applied to the Flexible Job Shop Scheduling Problem

Mekni, Souad; Chaâr, Besma Fayéch; Ksouri, Mekki

doi:10.3182/20100701-2-pt-4011.00059

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…The process of clustering sensor nodes into cover sets and scheduling them to maximize the lifespan of the network belongs to the NP-hard problems. This kind of problem can be solved using either exact methods, like linear programming and branch-bound or metaheuristics, namely genetic, invasive weed optimization, and Particle Swarm Optimization (PSO) algorithms [4][5][6][7][8]. The former approach requires an exponential computation time depending on the size of the problem to be solved, while the latter tries to obtain good solutions (not necessarily optimal) in a reasonable time.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Kamel,

Al Qahtani,

Al-Shahrani

2024

Eng. Technol. Appl. Sci. Res.

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Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

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