IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved combined with extremal (EO), named IFA-EO, where three strategies are incorporated. First, balance tradeoff between exploration ability exploitation ability, we adopt a new attraction model for operation, which combines full single through probability choice strategy. In model, small accepts worse solution improve diversity offspring. Second, adaptive step size proposed based on number iterations dynamically adjust attention or model. Third, combine EO powerful local-search FA. Experiments tested two group popular benchmarks including unimodal multimodal functions. Our experimental results demonstrate that IFA-EO can deal problems similar better performance than other eight variants, EO-based advanced differential evolution variant terms accuracy statistical results.