Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform

Sirisumrannukul, Somporn; Intaraumnauy, Tosapon; Piamvilai, Nattavit

doi:10.1016/j.heliyon.2024.e26937

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…In addition, particle swarm computing is often combined with other methods to improve the search performance and has been applied in various fields. Somporn Sirisumrannukul et al [34] combined artificial neural networks (ANNs) and PSO algorithms to not only collect real-time environmental data and air conditioner usage records, but also autonomously adjust the operation of air conditioners. Sathasivam Karthikeyan et al [35] adopted the artificial bee swarm (ABC) algorithm and PSO algorithm to optimize the Boost converter and improve the efficiency of the system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

Qin,

Zeng,

Sun

et al. 2024

Electronics

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In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

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Section: Introductionmentioning

confidence: 99%