Stud krill herd algorithm

• Published in: Neurocomputing (Amsterdam) Vol. 128; pp. 363 - 370
• Main Authors: Wang, Gai-Ge, Gandomi, Amir H., Alavi, Amir H.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 27.03.2014; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Genetics; Global optimization problem; Krill; Krill herd; Mathematical models; Multimodal function; Operators; Optimization; Reproduction; Stud genetic algorithm; Stud selection and crossover operator; Studs; Theoretical computing; Stud selection and crossover operator; Krill herd; Multimodal function; Stud genetic algorithm; Global optimization problem; Probabilistic approach; Local optimum; Updating; Global optimum; Population genetics; Optimization; Experimental result; Biomimetics; Genetic algorithm; Multimodality; Swarm intelligence; Artificial intelligence; Mathematical programming
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.08.031

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.