Krill herd: A new bio-inspired optimization algorithm |
| |
| Authors: | Amir Hossein Gandomi Amir Hossein Alavi |
| |
| Institution: | 1. Department of Civil Engineering, The University of Akron, Akron, OH, USA;2. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran;1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;2. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China;3. Department of Civil Engineering, University of Akron, Akron, OH 443253905, USA;4. Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI 48824, USA;5. Education College, Shihezi University, Shihezi, Xinjiang, 832000, China;1. Faculty of Engineering, Architecture, and Information Technology, University of Queensland, Brisbane, QLD 4072, Australia;2. Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C. 1983963113, Tehran, Iran;3. School of Information and Communication Technology, Griffith University, Nathan, Brisbane, QLD 4111, Australia;1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;2. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China |
| |
| Abstract: | 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
