Stud krill herd algorithm

doi:10.1016/j.neucom.2013.08.031

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Stud krill herd algorithm

2014-03-01

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Gai‐Ge Wang *

Amir H. Gandomi

Amir H. Alavi

DOI：10.1016/j.neucom.2013.08.031

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Abstract

En 中文

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This method is called the Stud Krill Herd (SKH). Similar to genetic reproduction mechanisms added to KH method, an updated genetic reproduction schemes, called stud selection and crossover (SSC) operator, is introduced into the KH during the krill updating process dealing with numerical optimization problems. The introduced SSC operator is originated from original Stud genetic algorithm. In SSC operator, the best krill, the Stud, provides its optimal information for all the other individuals in the population using general genetic operators instead of stochastic selection. This approach appears to be well capable of solving various functions. Several problems are used to test the SKH method. In addition, the influence of the different crossover types on convergence and performance is carefully studied. Experimental results indicate an instructive addition to the portfolio of swarm intelligence techniques. (C) 2013 Elsevier B.V. All rights reserved.

Keywords:

Global optimization problem

Krill herd

Stud genetic algorithm

Stud selection and crossover operator

Multimodal function