Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem

Rules embedded harris hawks optimizer for large-scale optimization problems

“…Other researchers suggested of embedding two different opposition-based schemes, namely selective, leading opposition, and the dynamic opposition technique. Enhancing HHO by implementing different random distribution functions which control the random movement of HHO agents was given by Akdag et al [ 40 ]. Specifically, they have investigated seven types of random distribution functions, including chi-square, normal, exponential, Rayleigh, student’s distribution, F-distribution, and lognormal.…”

“…Motivated by HHO popularity, simplicity, and efficiency, this study aims to further improve HHO performances when dealing with large-scale problems that encounter a lot of local optima points. It should be noted that previously mentioned studies mainly focused on enhancing HHO exploration by incorporating chaotic re-initialization schemes [ 43 ], embedding opposition-based schemes [ 36 , 39 ], or using other search operations inside HHO [ 33 , 33 – 35 , 40 ]. Others suggested using an external local search algorithm with HHO to improve exploitation performance [ 39 , 42 ].…”

“…These strategies are logarithmic spiral and opposition technique to improve the exploration performances. Rosenbrock local search was added to enhance the exploitation ability It was tested using IEEE CEC2014 benchmark and other engineering and real-world design problems [ 40 ] Akdag et al It incorporated various random distribution functions to enhance HHO It has been applied to optimize IEEE 30-bus power system. In addition, it was evaluated with the standard benchmark problems [ 41 ] Houssein et al It embedded genetic operators to enhance HHO exploitation performances It was evaluated using two chemoinformatics dataset namely QSAR Biodegradation and MAO [ 42 ] Krishna et al It triggered local pattern search algorithm during the exploitation phase of HHO It was tested using 23 standard CEC2005 benchmark and other engineering design problems [ 43 ] Singh et al It embedded logistic chaotic map to enhance the initialization and exploration ability of HHO It was evaluated using 12 UCI machine learning repository clustering problems …”

Güç Si̇stemleri̇nde Eypo Algori̇tmasi İle Voltaj Kararliliğinin İyi̇leşti̇rmesi̇

“…Bu metot yük akışını temel almaktadır. Yük akışı, güç sistemlerinde kullanılan ekipmanların istenilen limitler dâhilinde kalarak jeneratör ve baraların birbirleri arasında güç alışverişini sağlamasıdır (Akdag, Ates ve Yeroglu, 2021). Güç sistemlerinde, yük akışının optimal olarak sağlanması istenilir.…”

Optimizasyonun Optimizasyonu Yaklaşımıyla Dağılım Fonksiyonu Tabanlı Kral Kelebeği Optimizasyon Algoritmasının Performansının Artırılması

“…Bu algoritmalar içerisinde stokastik süreçler içeren nümerik optimizasyon algoritmaları önemli bir yer tutar ve günümüzde de çok geniş bir alanda kullanılır (Ates, 2021;Elbes et al, 2019). Bu tür algoritmaların birçok alandaki problemde kullanılmasının en önemli sebebi çoğu problemin belirsizlik içeren bir çözüm uzayına sahip olmasıdır (Akdag et al, 2020;A. Ateş & Yeroglu, 2016).…”