Krill herd: A new bio-inspired optimization algorithm

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.

[1]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[2]  I. Hampton,et al.  Krill aggregation characteristics: Spatial distribution patterns from hydroacoustic observations , 1989, Polar Biology.

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[5]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2011, Neural Computing and Applications.

[6]  S. Nicol,et al.  Living krill, zooplankton and experimental investigations: a discourse on the role of krill and their experimental study in marine ecology , 2003 .

[7]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[8]  Holly J. Price,et al.  Swimming behavior of krill in response to algal patches: A mesocosm study , 1989 .

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[11]  A. Singhal,et al.  Pattern matching in historical batch data using PCA , 2002 .

[12]  J Saunders,et al.  Parallelising a model of bacterial interaction and evolution. , 2004, Bio Systems.

[13]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[15]  J R Saunders,et al.  A rule-based approach to the modelling of bacterial ecosystems. , 2006, Bio Systems.

[16]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[17]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[18]  William Y. Brown,et al.  The Conservation of Antarctic Marine Living Resources , 1983, Environmental Conservation.

[19]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[20]  Peter J. Fleming,et al.  The Stud GA: A Mini Revolution? , 1998, PPSN.

[21]  A. Ōkubo.  Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. , 1986, Advances in biophysics.

[22]  Haiping Ma,et al.  An analysis of the equilibrium of migration models for biogeography-based optimization , 2010, Inf. Sci..

[23]  Q. H. Wu,et al.  Biologically inspired optimization: a review , 2009 .

[24]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[25]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[26]  Kevin Hapeshi,et al.  A Review of Nature-Inspired Algorithms , 2010 .

[27]  G. F.,et al.  From individuals to aggregations: the interplay between behavior and physics. , 1999, Journal of theoretical biology.

[28]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[29]  A. C. Hardy,et al.  Plankton Dynamics@@@The Plankton of the South Georgia Whaling Grounds and Adjacent Waters, 1926-27 , 1935 .

[30]  Amir Hossein Gandomi,et al.  Multi-stage genetic programming: A new strategy to nonlinear system modeling , 2011, Inf. Sci..

[31]  Julian Priddle,et al.  Scales of Interaction Between Antarctic Krill and the Environment , 1988 .

[32]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[33]  Cathy M. Lascara,et al.  Lagrangian modelling studies of Antarctic krill (Euphausia superba) swarm formation , 2004 .

[34]  J. Marr.  The natural history and geography of the Antarctic krill (Euphausia superba Dana) , 1961 .

[35]  Amir Hossein Gandomi,et al.  Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization , 2012, Comput. Math. Appl..