A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with levy flight

Koç, İsmail

doi:10.1016/j.eswa.2022.116835

Cited by 12 publications

(3 citation statements)

References 96 publications

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“…Beluga whales, such intelligent social creatures, share their geographical location to prey on each other. At the same time, to improve convergence, Levy's flight strategy is introduced [11]. The formula is as follows:…”

Section: Local Utilization Phasementioning

confidence: 99%

Short-term traffic flow prediction of BP based on adaptive BWO optimization

Mao¹,

Sun²,

Liu³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Accurate short-term traffic flow prediction is the basis and key to the intelligent transportation system. With the continuous development of machine learning algorithms and the latest swarm intelligence algorithms, a reasonable combination of the two will produce a good prediction effect. In this paper, BP neural network algorithm in the short-term traffic flow prediction problem accuracy is not high and easy to fall into the local minimum and so on. This paper established a BP based on adaptive BWO optimization short-term traffic flow prediction model, first of all, to carry on the data preprocessing the data set and divided into the training set and test set, and then the data for training, the best model to forecast practical optimization results, finally the model prediction results were compared with the rest of the 6 kinds of classical model. The experimental results show that the optimized BP model based on adaptive BWO can achieve a good traffic flow prediction effect in the short term, MAE is 7.357, MSE is 102.772, and 2 R is 0.889, which are better than the other six models.

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Section: Local Utilization Phasementioning

confidence: 99%

Short-term traffic flow prediction of BP based on adaptive BWO optimization

Mao¹,

Sun²,

Liu³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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“…İOP, acil durum araçları, sağlık merkezleri ve ticari banka şubeleri gibi tesis lokasyonunu problemlerini içermektedir (Baş ve Ülker, 2020;Koc, 2016). Buna ilaveten İOP bütçeleme, telekomünikasyon, toplu taşıma hizmetleri ve rüzgar türbini yerleştirme gibi zamanlama görevlerini de içermektedir (Koc, 2022;Korkmaz vd., 2018;Prescilla ve Selvakumar, 2013). Ayrıca İOP, sırt çantası problemi, kaynak tahsisi problemi, boyutsallık azaltma, özellik seçimi, ağ optimizasyonu, çok düzeyli görüntü eşik seçimi, eğri uydurma, birim bağlılığı ve hücre oluşumu gibi iyi bilinen NP-zor problemlerin çözümünde kullanılmaktadır (Arafat ve Moh, 2019;Coniglio vd., 2021;Inik vd., 2020;Koc vd., 2018;Zebari vd., 2020;Zhu vd., 2021) Kapasitesi olmayan tesis yerleşim problemi (Uncapacitated facility location problem -UFLP) literatürde hem klasik yöntemlerle hem de optimizasyon algoritmalarıyla ele alınmıştır (Chudak ve Shmoys, 2003;Ghosh, 2003).…”

Section: Giriş (Introduction)unclassified

Yeni̇ Bi̇r İki̇li̇ Sürüş Eği̇ti̇m Tabanli Algori̇tma Üzeri̇nde Transfer Fonksi̇yonlarinin İncelenmesi̇

Koç

2023

Mühendislik Bilimleri Ve Tasarım Dergisi

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Kapasitesiz Tesis Yerleşim Problemi (UFLP), tesislerin optimal yerleşimini belirleyen NP-zor bir problemdir. UFLP, NP-Zor problem grubundan olduğu için, bu problemlerin büyük örneklerini çözmek için kesin yöntemlerin kullanılması, optimal çözümü elde etmek için gereken yüksek hesaplama süreleri nedeniyle ciddi şekilde sorun teşkil edebilir. Bu çalışmada, problemin karmaşıklığından dolayı sürü zekası algoritması tercih edilmiştir. Son yıllarda sürüş eğitimi ilkelerine dayalı olarak geliştirilen popülasyon tabanlı bir algoritma olan Sürüş eğitim tabanlı (DTBO) algoritması UFLP probleminin çözümünde kullanılmıştır. DTBO’nun temel versiyonu sürekli problemlerin çözümünü ele aldığından söz konusu algoritmanın ikili problemlerin çözümüne uyarlanması gerekmektedir. Bunun için literatürde kullanılan dokuz farklı transfer fonksiyonu yardımıyla DTBO algoritması ikili problemlerin çözümüne uygun olarak tasarlanmıştır. Deneysel çalışmalar transfer fonksiyonlarının adil kıyaslanabilmesi için eşit koşullarda altında gerçekleştirilmiştir. Gerçekleştirilen deneysel çalışmalarda dokuz transfer fonksiyonu içerisinden ikili Mode-DTBO algoritmasının en başarılı algoritma olduğu görülmektedir. Bu sonuçlara göre Mode tabanlı DTBO algoritmasının küçük, orta ve büyük ölçekli tüm problem setlerinde hem çözüm kalitesi açısından hem de zaman açısından çok başarılı olduğu görülmektedir. Ayrıca DTBO algoritması IWO (Yabani Ot Algoritması – Invasive Weed Optimization) algoritmasına ait 3 farklı transfer fonksiyonuyla (Mode, Sigmoid ve Tanh) da kıyaslanmıştır. Karşılaştırmalı sonuçlar incelendiğinde 12 problemin 8’inde (orta ve büyük ölçekli problem) Mode-DTBO yaklaşımının IWO’ya ait 3 farklı yaklaşımın hepsinden çok daha başarılı olduğu görülmüştür. Bununla beraber, küçük boyutlu 4 problem üzerinde ise Mode fonksiyonunu kullanan her iki algoritmanın da optimal değeri yakaladığı görülmüştür. Sonuç olarak, Mode-DTBO yönteminin ikili problemlerin çözümünde çok etkili bir alternatif sunacağı söylenebilir.

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“…The main objectives of all these studies are focused on the enhancement of the power output in wind farm layouts. In this regard, more computation intelligence approaches have been improved and introduced to solve this problem such as: evolutionary algorithm (EA) [19], monte carlo simulation [20], greedy algorithm [21], simulated annealing (SA) [22], sequential convex programming [23], random search algorithm (RSA) [24], [25], [26], [27], multi-Objective random search algorithm (MORSA) [28], ant colony (AC) [29], ant lion optimization (ALO) [30], sparrow search algorithm (SSA) [31], single-objective hybrid optimizer (SOHO) [32], binary invasive weed optimization (BIWO) [33], [34], differential evo-lution(DE) [35], Jaya algorithm [36], integer programming [37], success history based adaptive differential evolution (L-SHADE) [38], cuckoo search (CS) [39], [40], biogeographybased optimization (BBO) [41], multi-team perturbationguiding jaya (MTPG-Jaya) [42], water cycle optimization (WCO) [43], dynastic optimization algorithm (DOA) [44], binary most valuable player algorithm (BMVPA) [45], adaptive neuro-fuzzy inference system (ANFIS) [46], extended pattern search algorithm (EPS) [47]. In this present study, the optimal wind turbine layout was for the first time performed based on a modified new inspired evolutionary algorithm recently developed in 2020 by Zhao et al [48]; named manta ray foraging optimization (MRFO).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

et al. 2022

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Manta ray foraging optimization (MRFO) algorithm is relatively a novel bio-inspired optimization technique directed to given real-world engineering problems. In this present work, wind turbines layout (WTs) inside a wind farm ,is considered as a real nonlinear optimization problem. In spite of the better convergence of MRFO, it gets stuck into local optima for large problems. The chaotic sequences are among the performed techniques used to tackle this shortcoming and improve the global search ability. Therefore, ten chaotic maps have been embedded into MRFO. To affirm the performance of the suggested chaotic approach CMRFO, it was first assessed using the IEEE CEC-2017 benchmark functions. This examination has been systematically compared to eight well-known optimization algorithms and the original MRFO. The non-parametric Wilcoxon statistical analysis significantly demonstrates the superiority of CMRFO as it ranks first in most test suites. Secondly, the MRFO and its best enhanced chaotic version were tested on the complex problem of finding the optimal locations of wind turbines within a wind farm. Besides, The application of the CMRFO to the wind farm layout optimization (WFLO) issue aims to minimize the cost per unit power output, and increase the electrical power engendered by all WTs and wind-farm efficiency. Two representative scenarios of the problem have been dealt with a square-shaped farm installed on an area of 2x2km, including variable wind direction with steady wind speed, and both wind direction and speed are variable. The WFLO outcomes reveal the CMRFO capability to find the optimal locations of WTs, that generate a maximum power for a minimum cost compared to three stochastic approaches and other previous studies. At last, the suggested CMRFO with Singer chaotic sequence has been successfully enhanced by accelerating the convergence, and providing better accuracy to find the global optimum.

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A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with levy flight

Cited by 12 publications

References 96 publications

Short-term traffic flow prediction of BP based on adaptive BWO optimization

Short-term traffic flow prediction of BP based on adaptive BWO optimization

Yeni̇ Bi̇r İki̇li̇ Sürüş Eği̇ti̇m Tabanli Algori̇tma Üzeri̇nde Transfer Fonksi̇yonlarinin İncelenmesi̇

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

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