A Hybrid Firefly Algorithm for Constrained optimization and Engineering Application

Firefly algorithm (FA) has been recently proposed as a stochastic optimization method and it is has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. FA as most population-based algorithm is good at identifying promising area of the search space, but less good at fine-tuning the approximation to the minimization. A novel hybrid firefly algorithm (HFA) based on Rosenbrock's local search method for constrained numerical and engineering optimization problem that relies on a feasibility-based rule for constraint-handling. Good-point-set method was used to initiate individual position, which strengthened the diversity of global searching. The comparison results with other stochastic optimization algorithms demonstrate that HFA with the embedded local search technique proves to be extremely effective and efficient at locating optimal solutions.