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Abstract
The Gbest-guided artificial bee colony (GABC) algorithm is a latest swarm intelligence-based approach to solve optimisation problem. In GABC, the individuals update their respective positions by drawing inspiration from the global best solution available in the current swarm. The GABC is a popular variant of the artificial bee colony (ABC) algorithm and is proved to be an efficient algorithm in terms of convergence speed. But, in this strategy, each individual is simply influenced by the global best solution, which may lead to trap in local optima. Therefore, in this paper, a new search strategy, namely “Fully Informed Learning” is incorporated in the onlooker bee phase of the ABC algorithm. The developed algorithm is named as fully informed artificial bee colony (FABC) algorithm. To validate the performance of FABC, it is tested on 20 well-known benchmark optimisation problems of different complexities. The results are compared with those of GABC and some more recent variants of ABC. The results are very promising and show that the proposed algorithm is a competitive algorithm in the field of swarm intelligence-based algorithms.
Disclosure statement
No potential conflict of interest was reported by the authors.