A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem

Bolaji, Asaju La’aro; Bamigbola, Akeem Femi; Adewole, Lawrence B; Shola, Peter Bamidele; Afolorunso, Adenrele; Obayomi, A.A.; Aremu, Dayo Reuben; Almazroi, Abdulwahab Ali

doi:10.1016/j.compbiomed.2022.105850

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…In this work, several population-based evolutionary algorithms, ABC, PSO, DE, ABC-OL, and PSO-OL (integrated with orthogonal learning [8]), are implemented and compared to evaluate their effectiveness. The basic ABC and PSO have been proven effective and implemented in various real-world optimization problem scenarios like scheduling problems, financial modeling, resource allocation, etc., thanks to their features such as ease of implementation, adaptability, good balance of exploration and exploitation, etc., [8,[24][25][26][27][28]. Thus, they were chosen as benchmarks, and future work can be focused on developing improved EAs to tackle these problems.…”

Section: Methodologiesmentioning

confidence: 99%

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

Bai,

Zhang,

Allmendinger

et al. 2024

Energies

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The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.

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Section: Methodologiesmentioning

confidence: 99%

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

Bai,

Zhang,

Allmendinger

et al. 2024

Energies

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“…İlk kez Karaboğa [19] tarafından literatüre kazandırılmış olan YAK optimizasyon algoritması arıların doğadaki yiyecek arama davranışlarından esinlenilerek geliştirilmiş olan bir Metasegisel Optimizasyon Algoritmasıdır. Nümerik problemlerde başarılı sonuçlar üreten YAK optimizasyon algoritması [20] daha sonraları birçok mühendislik problemine de uyarlanmış ve başarılı sonuçların elde edildiği görülmüştür [21][22][23].…”

Section: Yak Optimizasyon Algoritmasıunclassified

Farklı performans metriklerinin buharlaşma modellemesi üzerindeki etkisinin incelenmesi

Yilmaz

2023

NÖHÜ Müh. Bilim. Derg.

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Bu çalışmada Yapay Arı Kolonisi (YAK) optimizasyon algoritması ile Anamur meteoroloji istasyonu verileri kullanarak aylık toplam açık yüzey buharlaşması modellenmiştir. Çalışmalarda Determinasyon Katsayısı (R2), Nash–Sutcliffe Etkinlik katsayısı (NSE), Ortalama Karesel Hata (Mean Squared Error, MSE) ve Yanlılık Yüzdesi (PBIAS) metrikleri kullanılarak; R2 ve NSE maksimizasyonu ile MSE, MSE/R2, MSE/+NSE ve PBIAS minimizasyonu uygulanarak seçilen performans metriklerinin sonucu ne derecede etkilediği araştırılmıştır. Çalışmalar sonucunda öncelikle YAK algoritmasıyla başarılı buharlaşma modellerinin oluşturulabileceği görülmüş olup, seçilen performans metriklerinin sonucu önemli derecede etkilediği çıktısı elde edilmiştir. Kullanılan amaç fonksiyonları içerisinden R2 maksimizasyonu ile elde edilen modellerde düşük tahmin/yüksek tahmin probleminin meydana geldiği ve PBIAS minimizasyonunun ise oldukça başarısız modeller ürettiği görülmüş olup en başarılı modellerin MSE/+NSE amaç fonksiyonu ile elde edildiği sonucuna ulaşılmıştır. Bu alanda literatürde daha önce uygulanmamış olan MSE/+NSE metriğinin başarılı sonuçlar elde ettiğinin gösterilmiş olması mevcut çalışmanın ana çıktısı olarak kabul edilmekte ve bu durumun çalışmanın yenilikçi kısmını oluşturduğu düşünülmektedir.

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“…Appliances are ranked according to user preferences. In [21], the author compares greedy search and simulated annealing approaches with a GA-based methodology for maximum cutting costs in consumer devices. Although the GA-based model performed better in reducing costs, the study neglected the impact on user comfort.…”

Section: Related Workmentioning

confidence: 99%

“…It is also relevant to point out that the substantial reduction in PAR achieved by the SSWA method outperforms many existing strategies. Author in [20][21][22] discuss various techniques to reduce PAR in residential energy management. They highlight the potential of advanced scheduling algorithms, like SSWA, to provide superior PAR reduction compared to traditional methods.…”

Section: Plos Onementioning

confidence: 99%

Nature-inspired solutions for energy sustainability using novel optimization methods

Almazroi,

Ul Hassan

2023

PLoS ONE

Self Cite

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This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.

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A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem

Cited by 11 publications

References 25 publications

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

Farklı performans metriklerinin buharlaşma modellemesi üzerindeki etkisinin incelenmesi

Nature-inspired solutions for energy sustainability using novel optimization methods

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