Bee Colony Optimization with Applications in Transportation Engineering

Teodorović, Dušan; Nikolić, Miloš; Šelmić, Milica; Jovanović, Ivan

doi:10.1007/978-3-031-09835-2_7

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After PSO, various algorithms constructed on biological intelligence have been proposed and are used in the area of control engineering. These algorithms include ant Dorigo, Birattari, and Stutzle (2006), fish swarm optimization Li, Shao, and Qian (2002), artificial bee colony optimization Teodorovic and Dell'orco (2005), firefly algorithm Yang (2009), cuckoo search algorithm Gandomi et al (2013), bat algorithm Yang (2010), grey wolf algorithm Rezk et al (2020), and so forth. In Selvi and Umarani (2010) authors have investigated the drawbacks associated with the ant colony optimization algorithm (ACO).…”

Section: Preliminary Studiesmentioning

confidence: 99%

Modelling and control of fuzzy‐based systems using intelligent water drop algorithm

et al. 2022

View full text Add to dashboard Cite

Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques.

show abstract