A new firefly algorithm with mean condition partial attraction

As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effectively avoid slow convergence and poor stability. To optimize FA for the attraction model, a new FA with mean condition partial attraction is proposed (mcFA) in this paper. McFA, characterized by fast computing power, high precision, and easy implementation, is capable of remedying the defect that the FA is easy to converge slowly. As opposed to standard FA, mcFA has determined excellent model parameter values, and the mean condition partial attraction model is more suitable for different dimensional solutions than the full attraction model. Lastly, as verified by the theoretical and experimental results, mcFA outperforms other algorithms on most of the test functions. Moreover, the mean condition partial attraction model is shown to yield better solutions than the full attraction model.