Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Abstract

Meta-heuristic algorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired metaheuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

Kablosuz Algılayıcı Ağlarda Olasılıksal Kapsama Oranının Optimizasyonu için Yeni Bir Meta-Sezgisel Yaklaşım

“…Global optimizasyon amacıyla elektromanyetik olarak yüklü olan parçacıkların optimuma doğru hızlı bir Ģekilde yakınsamasını sağlayan ve bu parçacıklar arasındaki itme çekme hareketinden esinlenerek Birbil ve Fang [24] tarafından geliĢtirilip modellenen meta-sezgisel [26,27] ve popülasyon tabanlı bir algoritmadır. Doğrusal olmayan ve gerçek değerli problemlerin optimize edilmesi için tasarlanmasına rağmen sayısal problemler ile daha optimum sonuçlara ulaĢılabilmiĢtir [28].…”

Data Clustering Based on the Whale Optimization

“…Yapay Arı Algoritması [5], Cırcır Böceği Algoritması [7] gibi sürü temelli olan bu algoritma kendine özgü birçok özelliğe sahiptir.…”

“…Literatüre baktığımızda bu yöntemlerden meta-sezgisel algoritmalardan verimli sonuçlar elde edildiğini görmek mümkündür. Esinlenildiği kaynaklara göre sürü tabanlı [4][5][6][7], fizik tabanlı [8,9], kimya tabanlı [10,11], gibi alt kategorilere ayrılan bu yöntemler klasik matematiksel yöntemlerle çözümü zor olan birçok probleme optimum çözümler sunabilmektedir. Çözüm sunduğu problemler arasında veri kümeleme işlemi de bulunmaktadır.…”

Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

“…In general, they propose new models and innovative evolutionary operators for producing an adequate exploration and exploitation of large search spaces considering a significant number of dimensions. This new generation of metaheuristic approaches is large, some examples include Crow Search Algorithm (CSA) [17], Cuckoo Search (CS) [18], Moth-flame Optimization Algorithm (MFOA) [19], Grey Wolf Optimizer (GWO) [20], Whale Optimization Algorithm (WOA) [21], Gravitational Search Algorithm (GSA) [22], Sine Cosine Algorithm (SCA) [23], Cricket Behavior-Based Evolutionary (CBE) [24], Dragonfly Algorithm (DF) [25], Hybrid Cognitive Optimization Algorithm (HCOA) [26], Interactive Search Algorithm (ISA) [27], Farmland Fertility Algorithm (FFA) [28], Binary Artificial Algae Algorithm (BAAA) [29], Electro-Search algorithm (ESA) [30], Island Bat Algorithm for Optimization (IBAO) [31], to name a few.…”