Adaptive firefly algorithm with alternative search
Firefly algorithm (FA) is a population based stochastic algorithm, which is inspired by the behavior of the flashing of fireflies. Though some recent studies show that FA is effective on many optimization problems, its performance is greatly influenced by its control parameters. In this paper, a new FA called adaptive FA with alternative search (AFAas) is proposed to improve the performance of FA. The main contribution of this paper consists of two aspects: 1) an adaptive strategy is used to dynamically adjust the control parameters; and 2) an alternative search strategy is employed to enhance the global and local search abilities. Experiments are conducted on a set of well known benchmark functions. Computational results show that our approach AFAas achieves better solutions than the standard FA and some recently published FA variants.
2016 IEEE Congress on Evolutionary Computation (CEC)