Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
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Artificial bee colony (ABC) algorithm is relatively a new bio-inspired swarm intelligence optimization technique comparative to other population based algorithms. In this study BGA (breeder GA) mutation is embedded into onlooker bee phase to improve the capability of local search. The proposed variant is named B-ABC. The experimental results on 10 constrained benchmark functions demonstrate the performance of the proposed variant against those of state-of-the-art algorithms for a set of constrained test problems. Further the efficiency of the proposed variant is tested on the car side impact problem.