Objectives: The objective of this work is to fine tune the variant of FA (Firefly Algorithm), NaFA (Firefly Algorithm with neighbourhood Attraction) by parameter tuning such as a, b , g . Furthermore, a new variant called Step Size Modified FA with neighbourhood attraction (SSMFA-N) has been proposed in which the step size is updated during the algorithm run so that a balance between local and global search is achieved. The considered objective functions are PO and ITAE. Methods: It is well known that parameters that are considered initially for any metaheuristic algorithms are purely trial and error basis and this leads to erroneous optimized results. While analysing the algorithms, NaFA (a variant of FA) has been considered for efficient convergence and good performance. On analysing it is noticed that FA and its variants’ performances and convergence depend on Step Size(a), brightness (b ) and adsorption coefficient(g ). In both the FA and NaFA the parameters to be tuned for effective convergence are a, b and g . It is also understood that the parameter b had been done separately and the parameters a and g cannot be fine-tuned simultaneously. Therefore, in NaFA the parameter tuning for a has been done for the processes FOPDT, stable and unstable SOPDT and the new variant SSMFA-N is thus proposed. Findings: The algorithm is made to run in MATLAB and Simulink environment for three different processes such as FOPDT, Stable and Unstable Second order Process (bioreactor processes) with the objective functions of less PO and ITAE. The obtained results from all the three processes are compared with the conventional and optimization methods (PSO) and shown that SSMFA-N outperforms the conventional and optimization approaches in both the time domain and performance indices. Novelty: The novelty is the modification of step size of NaFA, which ultimately leads to a new variant called SSMFA-N. Keywords: FA; FA with neighbourhood attraction; Step size tuning; Bioreactor Process