Krill herd: A new bio-inspired optimization algorithm

► A new bio-inspired algorithm, namely krill herd (KH) is proposed for global optimization. ► The time-dependent position of the krill individuals is formulated by three main factors. ► The KH algorithm has a better performance than well-known methods in the literature. In this paper, a novel biolog...

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Vydané v:Communications in nonlinear science & numerical simulation Ročník 17; číslo 12; s. 4831 - 4845
Hlavní autori: Gandomi, Amir Hossein, Alavi, Amir Hossein
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2012
Predmet:
Algorithms
Benchmarking
Biologically-inspired algorithm
Computer simulation
Density
Krill
Krill herd
Mathematical models
Metaheuristic
Movement
Optimization
Benchmarking
Metaheuristic
Krill herd
Biologically-inspired algorithm
Optimization
ISSN:1007-5704, 1878-7274
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Shrnutí:► A new bio-inspired algorithm, namely krill herd (KH) is proposed for global optimization. ► The time-dependent position of the krill individuals is formulated by three main factors. ► The KH algorithm has a better performance than well-known methods in the literature. In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
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ISSN:1007-5704
1878-7274
DOI:10.1016/j.cnsns.2012.05.010