The theory of evolution strategies
The theory of evolution strategies
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
The Stud GA: A Mini Revolution?
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization
Journal of Global Optimization
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
A new optimization method: Big Bang-Big Crunch
Advances in Engineering Software
Performance optimization of gas turbine engine
Engineering Applications of Artificial Intelligence
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
Charged system search for optimal design of frame structures
Applied Soft Computing
Computers & Mathematics with Applications
A two-stage genetic algorithm for automatic clustering
Neurocomputing
Active leading through obstacles using ant-colony algorithm
Neurocomputing
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Swarm Intelligence and Bio-Inspired Computation: Theory and Applications
Swarm Intelligence and Bio-Inspired Computation: Theory and Applications
Metaheuristic Applications in Structures and Infrastructures
Metaheuristic Applications in Structures and Infrastructures
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Information Sciences: an International Journal
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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.