Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm

Etaati, Bahareh; Neshat, Mehdi; Dehkordi, Amin Abdollahi; Pargoo, Navid Salami; El-Abd, Mohammed; Sadollah, Ali; Gandomi, Amir H.

doi:10.1016/j.rineng.2024.101859

Cited by 6 publications

(1 citation statement)

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“…The essence of metaheuristic algorithms lies in their ability to strike a delicate balance between exploration and exploitation [19]. Exploration involves systematically searching the solution space to discover new potential solutions, while exploitation focuses on refining and exploiting promising solutions to improve overall performance [20]. This balance is crucial in achieving a convergence of the algorithm towards optimal solutions while avoiding premature convergence to suboptimal solutions [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Al-Betar,

Abasi,

Alyasseri

et al. 2024

Algorithms

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The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints in geographically dispersed settings. Federated learning (FL) emerges as a transformative paradigm for sustainable development by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability and sustainability. This paper introduces an innovative FL framework that enhances communication efficiency. The proposed framework addresses the communication bottleneck by harnessing the power of the Lemurs optimizer (LO), a nature-inspired metaheuristic algorithm. Inspired by the cooperative foraging behavior of lemurs, the LO strategically selects the most relevant model updates for communication, significantly reducing communication overhead. The framework was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, and waste recycling plant datasets representing various areas of sustainable development. Experimental results demonstrate that the proposed framework reduces communication overhead by over 15% on average compared to baseline FL approaches, while maintaining high model accuracy. This breakthrough extends the applicability of FL to resource-constrained environments, paving the way for more scalable and sustainable solutions for real-world initiatives.

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