A Survey on Dragonfly Algorithm and its Applications in Engineering

Abstract

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

Rüzgar hız dağılımı modelinin Yusufcuk algoritması ile parametre tahminlemesi

“…Optimizasyon işlemi devam ettiği sürece kümelerdeki her bir Yusufçuğun pozisyonu bireysel olarak güncellenmektedir. YA'nın program akışı Tablo 2'de verilmektedir [16,26,27]. YA, ayrılma (separation), hizalama (alignment), uyum (cohesion) şeklinde üç temel operatöre sahiptir.…”

“…Rüzgar enerjisi kaynak değerlendirmesinde doğru parametre tahmini büyük önem taşıdığından, rüzgar karakterizasyonu için farklı OYF'ların yanı sıra optimum parametrelerin tahmini için Sosyal Örümcek Optimizasyonu (SÖO) adı verilen yeni bir meta-sezgisel optimizasyon algoritması da önerilmiştir [13]. Mühendislik çalışmalarında Tavuk Sürüsü Optimizasyonu (TSO) [14], PSO, Gri Kurt Optimizasyonu (GKO) [15] ve Yusufçuk Algoritması (YA) [16] gibi sürü algoritmaları çok aktif bir araştırma alanı haline gelmiştir.…”

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

“…O (nm2) [74] O (HMS X M + HMS X log (HMS) [78] O (mn2) [82] Convergence Fastest rate [54] Good convergence [59] Better convergence [62] Slow rate [65] Fast rate [70] Quickly rate [75] Suffer from permanent convergence [79] Rapidly converged [82] Strength The balance between exploration and exploitation [55] The balance between exploration and exploitation [57] The balance between exploration and exploitation [61] The balance between intensification and diversification [66] Deal with complex fitness landscape [71] Do not have overlapping and mutation calculation [76] Increase the diversity of the new solutions [80] Avoid trapped at local optimum [83] Weaknesses Low precision [56] High precision [60] High accuracy [61] Trapped in a local optimum [67] Evaluation is relatively expensive [72] Suffers from partial optimism [76] Get stuck on local optimal [80] Needs huge memory resources…”

Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions

“…ese algorithms are: bat algorithm (BAT) [3], cuckoo search (CS) [4], ant lion optimizer (ALO) [5,6], elephant herding optimisation (EHO) [7,8], mothame optimisation (MFO) [9], krill herd (KH) [10], moth search algorithm (MSA) [11], monarch butter y optimisation (MBO) [12,13], mussels wandering optimisation (MWO) [14], and whale optimisation algorithm (WOA) [15]. Other meta-heuristic optimisation algorithms such as di erential evolution (DE) [16], biogeography-based optimisation (BBO) [17,18], harmony search (HS) [19], evolution strategies (ES) [20], sine cosine algorithm (SCA) [21], gravitational search algorithm (GSA) [22], monkey algorithm [23,24], dragon y algorithm (DA), and hybrid ABC/ DA (HAD) [25,26] are also e ciently used for solving many optimisation problems.…”